Refactor training, with new spacy.train module. Defaults still a little awkward.

bin/parser/train.py

@@ -79,82 +79,23 @@ def _merge_sents(sents):
     return [(m_deps, m_brackets)]
 
 
-def train(Language, gold_tuples, model_dir, tagger_cfg, parser_cfg, entity_cfg,
+def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
         n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
-    dep_model_dir = path.join(model_dir, 'deps')
-    ner_model_dir = path.join(model_dir, 'ner')
-    pos_model_dir = path.join(model_dir, 'pos')
-    if path.exists(dep_model_dir):
-        shutil.rmtree(dep_model_dir)
-    if path.exists(ner_model_dir):
-        shutil.rmtree(ner_model_dir)
-    if path.exists(pos_model_dir):
-        shutil.rmtree(pos_model_dir)
-    os.mkdir(dep_model_dir)
-    os.mkdir(ner_model_dir)
-    os.mkdir(pos_model_dir)
-
-    if parser_cfg['pseudoprojective']:
-        # preprocess training data here before ArcEager.get_labels() is called
-        gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
-
-    parser_cfg['labels'] = ArcEager.get_labels(gold_tuples)
-    entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
-
-    with (dep_model_dir / 'config.json').open('w') as file_:
-        json.dump(file_, parser_config)
-    with (ner_model_dir / 'config.json').open('w') as file_:
-        json.dump(file_, entity_config)
-    with (pos_model_dir / 'config.json').open('w') as file_:
-        json.dump(file_, tagger_config)
-
-    if n_sents > 0:
-        gold_tuples = gold_tuples[:n_sents]
-
-    nlp = Language(
-            data_dir=model_dir,
-            tagger=Tagger.blank(nlp.vocab, **tagger_cfg),
-            parser=Parser.blank(nlp.vocab, ArcEager, **parser_cfg),
-            entity=Parser.blank(nlp.vocab, BiluoPushDown, **entity_cfg))
     print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
-    for itn in range(n_iter):
+    format_str = '{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
-        scorer = Scorer()
+    with Language.train(model_dir, train_data,
+            tagger_cfg, parser_cfg, entity_cfg) as trainer:
         loss = 0
-        for raw_text, sents in gold_tuples:
+        for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
-            if gold_preproc:
+            for doc, gold in epoch:
-                raw_text = None
+                trainer.update(doc, gold)
-            else:
+            dev_scores = trainer.evaluate(dev_data)
-                sents = _merge_sents(sents)
+            print(format_str.format(itn, loss, **dev_scores.scores))
-            for annot_tuples, ctnt in sents:
-                if len(annot_tuples[1]) == 1:
-                    continue
-                score_model(scorer, nlp, raw_text, annot_tuples,
-                            verbose=verbose if itn >= 2 else False)
-                if raw_text is None:
-                    words = add_noise(annot_tuples[1], corruption_level)
-                    tokens = nlp.tokenizer.tokens_from_list(words)
-                else:
-                    raw_text = add_noise(raw_text, corruption_level)
-                    tokens = nlp.tokenizer(raw_text)
-                nlp.tagger(tokens)
-                gold = GoldParse(tokens, annot_tuples)
-                if not gold.is_projective:
-                    raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
-                loss += nlp.parser.train(tokens, gold)
-                nlp.entity.train(tokens, gold)
-                nlp.tagger.train(tokens, gold.tags)
-        random.shuffle(gold_tuples)
-        print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
-                                                   scorer.tags_acc,
-                                                   scorer.token_acc))
-    print('end training')
-    nlp.end_training(model_dir)
-    print('done')
 
 
 def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
              beam_width=None, cand_preproc=None):
-    nlp = Language(data_dir=model_dir)
+    nlp = Language(path=model_dir)
     if nlp.lang == 'de':
         nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
     if beam_width is not None:
@@ -227,9 +168,13 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
 
     lang = spacy.util.get_lang_class(language)
     
+    parser_cfg['features'] = lang.Defaults.parser_features
+    entity_cfg['features'] = lang.Defaults.entity_features
+
     if not eval_only:
         gold_train = list(read_json_file(train_loc))
-        train(lang, gold_train, model_dir, tagger_cfg, parser_cfg, entity_cfg,
+        gold_dev = list(read_json_file(dev_loc))
+        train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
               n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
               n_iter=n_iter)
     if out_loc:

spacy/en/__init__.py

@@ -27,4 +27,3 @@ class English(Language):
         tag_map = dict(language_data.TAG_MAP)
 
         stop_words = set(language_data.STOP_WORDS)
-

spacy/gold.pyx

@@ -1,3 +1,5 @@
+from __future__ import unicode_literals, print_function
+
 import numpy
 import io
 import json
@@ -128,7 +130,6 @@ def _min_edit_path(cand_words, gold_words):
 
 
 def read_json_file(loc, docs_filter=None):
-    print loc
     if path.isdir(loc):
         for filename in os.listdir(loc):
             yield from read_json_file(path.join(loc, filename))
@@ -199,7 +200,7 @@ def _consume_ent(tags):
 
 
 cdef class GoldParse:
-    def __init__(self, tokens, annot_tuples, brackets=tuple(), make_projective=False):
+    def __init__(self, tokens, annot_tuples, make_projective=False):
         self.mem = Pool()
         self.loss = 0
         self.length = len(tokens)
@@ -209,9 +210,6 @@ cdef class GoldParse:
         self.c.heads = <int*>self.mem.alloc(len(tokens), sizeof(int))
         self.c.labels = <int*>self.mem.alloc(len(tokens), sizeof(int))
         self.c.ner = <Transition*>self.mem.alloc(len(tokens), sizeof(Transition))
-        self.c.brackets = <int**>self.mem.alloc(len(tokens), sizeof(int*))
-        for i in range(len(tokens)):
-            self.c.brackets[i] = <int*>self.mem.alloc(len(tokens), sizeof(int))
 
         self.tags = [None] * len(tokens)
         self.heads = [None] * len(tokens)
@@ -246,14 +244,6 @@ cdef class GoldParse:
             proj_heads,_ = nonproj.PseudoProjectivity.projectivize(self.heads,self.labels)
             self.heads = proj_heads
 
-        self.brackets = {}
-        for (gold_start, gold_end, label_str) in brackets:
-            start = self.gold_to_cand[gold_start]
-            end = self.gold_to_cand[gold_end]
-            if start is not None and end is not None:
-                self.brackets.setdefault(start, {}).setdefault(end, set())
-                self.brackets[end][start].add(label_str)
-
     def __len__(self):
         return self.length
 

spacy/language.py

@@ -2,6 +2,8 @@ from __future__ import absolute_import
 from __future__ import unicode_literals
 from warnings import warn
 import pathlib
+from contextlib import contextmanager
+import shutil
 
 try:
     import ujson as json
@@ -15,7 +17,6 @@ except NameError:
     basestring = str
 
 
-
 from .tokenizer import Tokenizer
 from .vocab import Vocab
 from .syntax.parser import Parser
@@ -27,9 +28,12 @@ from .syntax.ner import BiluoPushDown
 from .syntax.arc_eager import ArcEager
 from . import util
 from .lemmatizer import Lemmatizer
+from .train import Trainer
 
 from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
 from .syntax.parser import get_templates
+from .syntax.nonproj import PseudoProjectivity
+
 
 
 class BaseDefaults(object):
@@ -84,46 +88,62 @@ class BaseDefaults(object):
                                   suffix_search=suffix_search,
                                   infix_finditer=infix_finditer)
         else:
-            return Tokenizer(vocab, rules=rules,
+            tokenizer =  Tokenizer(vocab, rules=rules,
                     prefix_search=prefix_search, suffix_search=suffix_search,
                     infix_finditer=infix_finditer)
+            return tokenizer
 
-    def Tagger(self, vocab):
+    def Tagger(self, vocab, **cfg):
         if self.path:
             return Tagger.load(self.path / 'pos', vocab)
         else:
             return Tagger.blank(vocab, Tagger.default_templates())
 
-    def Parser(self, vocab, blank=False):
+    def Parser(self, vocab, **cfg):
-        if blank:
+        if self.path and (self.path / 'dep').exists():
-            return Parser.blank(vocab, ArcEager,
+            return Parser.load(self.path / 'dep', vocab, ArcEager)
-                features=self.parser_features, labels=self.parser_labels)
-        elif self.path and (self.path / 'deps').exists():
-            return Parser.load(self.path / 'deps', vocab, ArcEager)
         else:
-            return None
+            if 'features' not in cfg:
+                cfg['features'] = self.parser_features
+            if 'labels' not in cfg:
+                cfg['labels'] = self.parser_labels
+            return Parser.blank(vocab, ArcEager, **cfg)
 
-    def Entity(self, vocab, blank=False):
+    def Entity(self, vocab, **cfg):
-        if blank:
+        if self.path and (self.path / 'ner').exists():
-            return Parser.blank(vocab, BiluoPushDown,
-                features=self.entity_features, labels=self.entity_labels)
-        elif self.path and (self.path / 'ner').exists():
             return Parser.load(self.path / 'ner', vocab, BiluoPushDown)
         else:
-            return None
+            if 'features' not in cfg:
+                cfg['features'] = self.entity_features
+            if 'labels' not in cfg:
+                cfg['labels'] = self.entity_labels
+            return Parser.blank(vocab, BiluoPushDown, **cfg)
 
-    def Matcher(self, vocab):
+    def Matcher(self, vocab, **cfg):
         if self.path:
             return Matcher.load(self.path, vocab)
         else:
             return Matcher(vocab)
 
-    def Pipeline(self, nlp):
+    def Pipeline(self, nlp, **cfg):
-        return [
+        pipeline = [nlp.tokenizer]
-            nlp.tokenizer,
+        if nlp.tagger:
-            nlp.tagger,
+            pipeline.append(nlp.tagger)
-            nlp.parser,
+        if nlp.parser:
-            nlp.entity]
+            pipeline.append(nlp.parser)
+        if nlp.entity:
+            pipeline.append(nlp.entity)
+        return pipeline
+    
+    prefixes = tuple()
+
+    suffixes = tuple()
+
+    infixes = tuple()
+ 
+    tag_map = {}
+
+    tokenizer_exceptions = {}
    
     parser_labels = {0: {'ROOT': True}}
 
@@ -169,6 +189,58 @@ class Language(object):
     Defaults = BaseDefaults
     lang = None
 
+    @classmethod
+    def blank(cls):
+        return cls(path=False, vocab=False, tokenizer=False, tagger=False,
+                   parser=False, entity=False, matcher=False, serializer=False,
+                   vectors=False, pipeline=False)
+
+    @classmethod
+    @contextmanager
+    def train(cls, path, gold_tuples, *configs):
+        if isinstance(path, basestring):
+            path = pathlib.Path(path)
+        tagger_cfg, parser_cfg, entity_cfg = configs
+        dep_model_dir = path / 'dep'
+        ner_model_dir = path / 'ner'
+        pos_model_dir = path / 'pos'
+        if dep_model_dir.exists():
+            shutil.rmtree(str(dep_model_dir))
+        if ner_model_dir.exists():
+            shutil.rmtree(str(ner_model_dir))
+        if pos_model_dir.exists():
+            shutil.rmtree(str(pos_model_dir))
+        dep_model_dir.mkdir()
+        ner_model_dir.mkdir()
+        pos_model_dir.mkdir()
+
+        if parser_cfg['pseudoprojective']:
+            # preprocess training data here before ArcEager.get_labels() is called
+            gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
+
+        parser_cfg['labels'] = ArcEager.get_labels(gold_tuples)
+        entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
+
+        with (dep_model_dir / 'config.json').open('wb') as file_:
+            json.dump(parser_cfg, file_)
+        with (ner_model_dir / 'config.json').open('wb') as file_:
+            json.dump(entity_cfg, file_)
+        with (pos_model_dir / 'config.json').open('wb') as file_:
+            json.dump(tagger_cfg, file_)
+
+        self = cls.blank()
+        self.path = path
+        self.vocab = self.defaults.Vocab()
+        self.defaults.parser_labels = parser_cfg['labels']
+        self.defaults.entity_labels = entity_cfg['labels']
+        self.tokenizer = self.defaults.Tokenizer(self.vocab)
+        self.tagger = self.defaults.Tagger(self.vocab, **tagger_cfg)
+        self.parser = self.defaults.Parser(self.vocab, **parser_cfg)
+        self.entity = self.defaults.Entity(self.vocab, **entity_cfg)
+        self.pipeline = self.defaults.Pipeline(self)
+        yield Trainer(self, gold_tuples)
+        self.end_training()
+
     def __init__(self,
         path=None,
         vocab=True,
@@ -210,13 +282,19 @@ class Language(object):
         self.path = path
         defaults = defaults if defaults is not True else self.get_defaults(self.path)
         
+        self.defaults = defaults
         self.vocab     = vocab if vocab is not True else defaults.Vocab(vectors=vectors)
         self.tokenizer = tokenizer if tokenizer is not True else defaults.Tokenizer(self.vocab)
         self.tagger    = tagger if tagger is not True else defaults.Tagger(self.vocab)
         self.entity    = entity if entity is not True else defaults.Entity(self.vocab)
         self.parser    = parser if parser is not True else defaults.Parser(self.vocab)
         self.matcher   = matcher if matcher is not True else defaults.Matcher(self.vocab)
-        self.pipeline  = pipeline(self) if pipeline is not True else defaults.Pipeline(self)
+        if pipeline in (None, False):
+            self.pipeline = []
+        elif pipeline is True:
+            self.pipeline = defaults.Pipeline(self)
+        else:
+            self.pipeline = pipeline(self)
 
     def __reduce__(self):
         args = (
@@ -276,15 +354,18 @@ class Language(object):
     def end_training(self, path=None):
         if path is None:
             path = self.path
-        if self.parser:
+        elif isinstance(path, basestring):
-            self.parser.model.end_training()
+            path = pathlib.Path(path)
-            self.parser.model.dump(path / 'deps' / 'model')
+        
-        if self.entity:
-            self.entity.model.end_training()
-            self.entity.model.dump(path / 'ner' / 'model')
         if self.tagger:
             self.tagger.model.end_training()
-            self.tagger.model.dump(path / 'pos' / 'model')
+            self.tagger.model.dump(str(path / 'pos' / 'model'))
+        if self.parser:
+            self.parser.model.end_training()
+            self.parser.model.dump(str(path / 'dep' / 'model'))
+        if self.entity:
+            self.entity.model.end_training()
+            self.entity.model.dump(str(path / 'ner' / 'model'))
         
         strings_loc = path / 'vocab' / 'strings.json'
         with strings_loc.open('w', encoding='utf8') as file_:
@@ -307,7 +388,7 @@ class Language(object):
         else:
             entity_iob_freqs = []
             entity_type_freqs = []
-        with (path / 'vocab' / 'serializer.json').open('w') as file_:
+        with (path / 'vocab' / 'serializer.json').open('wb') as file_:
             file_.write(
                 json.dumps([
                     (TAG, tagger_freqs),

spacy/scorer.py

@@ -70,6 +70,15 @@ class Scorer(object):
     def ents_f(self):
         return self.ner.fscore * 100
 
+    @property
+    def scores(self):
+        return {
+            'uas': self.uas, 'las': self.las,
+            'ents_p': self.ents_p, 'ents_r': self.ents_r, 'ents_f': self.ents_f,
+            'tags_acc': self.tags_acc,
+            'token_acc': self.token_acc
+        }
+
     def score(self, tokens, gold, verbose=False, punct_labels=('p', 'punct')):
         assert len(tokens) == len(gold)
 

spacy/strings.pxd

@@ -1,11 +1,11 @@
+from libc.stdint cimport int64_t
+
 from cymem.cymem cimport Pool
 from preshed.maps cimport PreshMap
 from murmurhash.mrmr cimport hash64
-from .typedefs cimport attr_t
 
-from libc.stdint cimport int64_t
+from .typedefs cimport attr_t, hash_t
 
-from .typedefs cimport hash_t
 
 cpdef hash_t hash_string(unicode string) except 0
 

spacy/syntax/arc_eager.pyx

@@ -312,12 +312,6 @@ cdef class ArcEager(TransitionSystem):
                 # Count frequencies, for use in encoder
                 self.freqs[HEAD][gold.c.heads[i] - i] += 1
                 self.freqs[DEP][gold.c.labels[i]] += 1
-        for end, brackets in gold.brackets.items():
-            for start, label_strs in brackets.items():
-                gold.c.brackets[start][end] = 1
-                for label_str in label_strs:
-                    # Add the encoded label to the set
-                    gold.brackets[end][start].add(self.strings[label_str])
 
     cdef Transition lookup_transition(self, object name) except *:
         if '-' in name:

spacy/syntax/parser.pyx

@@ -83,8 +83,7 @@ cdef class Parser:
         with (path / 'config.json').open() as file_:
             cfg = json.load(file_)
         moves = moves_class(vocab.strings, cfg['labels'])
-        templates = get_templates(cfg['features'])
+        model = ParserModel(cfg['features'])
-        model = ParserModel(templates)
         if (path / 'model').exists():
             model.load(str(path / 'model'))
         return cls(vocab, moves, model, **cfg)
@@ -96,7 +95,6 @@ cdef class Parser:
         model = ParserModel(templates)
         return cls(vocab, moves, model, **cfg)
 
-
     def __init__(self, Vocab vocab, transition_system, ParserModel model, **cfg):
         self.moves = transition_system
         self.model = model
@@ -191,7 +189,7 @@ cdef class Parser:
         free(eg.is_valid)
         return 0
   
-    def train(self, Doc tokens, GoldParse gold):
+    def update(self, Doc tokens, GoldParse gold):
         self.moves.preprocess_gold(gold)
         cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
         self.moves.initialize_state(stcls.c)

spacy/tagger.pyx

@@ -154,7 +154,7 @@ cdef class Tagger:
             model.load(str(path / 'model'))
         return cls(vocab, model)
 
-    def __init__(self, Vocab vocab, TaggerModel model):
+    def __init__(self, Vocab vocab, TaggerModel model, **cfg):
         self.vocab = vocab
         self.model = model
         # TODO: Move this to tag map
@@ -208,7 +208,9 @@ cdef class Tagger:
             self(doc)
             yield doc
     
-    def train(self, Doc tokens, object gold_tag_strs):
+    def update(self, Doc tokens, object gold):
+        if hasattr(gold, 'tags'):
+            gold_tag_strs = list(gold.tags)
         assert len(tokens) == len(gold_tag_strs)
         for tag in gold_tag_strs:
             if tag != None and tag not in self.tag_names:

spacy/train.py

@@ -0,0 +1,59 @@
+from __future__ import absolute_import
+from __future__ import unicode_literals
+
+import random
+from .gold import GoldParse
+from .scorer import Scorer
+
+
+class Trainer(object):
+    def __init__(self, nlp, gold_tuples):
+        self.nlp = nlp
+        self.gold_tuples = gold_tuples
+
+    def epochs(self, nr_epoch, augment_data=None):
+        def _epoch():
+            for raw_text, paragraph_tuples in self.gold_tuples:
+                if augment_data is not None:
+                    raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
+                docs = self.make_docs(raw_text, paragraph_tuples)
+                golds = self.make_golds(docs, paragraph_tuples)
+                for doc, gold in zip(docs, golds):
+                    yield doc, gold
+
+        for itn in range(nr_epoch):
+            random.shuffle(self.gold_tuples)
+            yield _epoch()
+ 
+    def update(self, doc, gold):
+        for process in self.nlp.pipeline[1:]:
+            if hasattr(process, 'update'):
+                process.update(doc, gold)
+            process(doc)
+        return doc
+
+    def evaluate(self, dev_sents):
+        scorer = Scorer()
+        for raw_text, paragraph_tuples in dev_sents:
+            docs = self.make_docs(raw_text, paragraph_tuples)
+            golds = self.make_golds(docs, paragraph_tuples)
+            for doc, gold in zip(docs, golds):
+                for process in self.nlp.pipeline[1:]:
+                    process(doc)
+                scorer.score(doc, gold)
+        return scorer
+
+    def make_docs(self, raw_text, paragraph_tuples):
+        if raw_text is not None:
+            return [self.nlp.tokenizer(raw_text)]
+        else:
+            return [self.nlp.tokenizer.tokens_from_list(sent_tuples[0][1])
+                    for sent_tuples in paragraph_tuples]
+
+    def make_golds(self, docs, paragraph_tuples):
+        if len(docs) == 1:
+            return [GoldParse(docs[0], sent_tuples[0])
+                    for sent_tuples in paragraph_tuples]
+        else:
+            return [GoldParse(doc, sent_tuples[0])
+                    for doc, sent_tuples in zip(docs, paragraph_tuples)]

spacy/util.py

@@ -13,6 +13,7 @@ try:
 except NameError:
     basestring = str
 
+
 LANGUAGES = {}
 _data_path = pathlib.Path(__file__).parent / 'data'
 

spacy/vocab.pyx

@@ -177,7 +177,7 @@ cdef class Vocab:
                     value = self.strings[value]
                 if attr == PROB:
                     lex.prob = value
-                else:
+                elif value is not None:
                     Lexeme.set_struct_attr(lex, attr, value)
         if is_oov:
             lex.id = 0