Merge branch 'develop' of https://github.com/explosion/spaCy into develop

spacy/__main__.py

@@ -7,7 +7,7 @@ if __name__ == '__main__':
     import plac
     import sys
     from spacy.cli import download, link, info, package, train, convert, model
-    from spacy.cli import profile
+    from spacy.cli import profile, evaluate
     from spacy.util import prints
 
     commands = {
@@ -15,6 +15,7 @@ if __name__ == '__main__':
         'link': link,
         'info': info,
         'train': train,
+        'evaluate': evaluate,
         'convert': convert,
         'package': package,
         'model': model,

spacy/cli/__init__.py

@@ -4,5 +4,6 @@ from .link import link
 from .package import package
 from .profile import profile
 from .train import train
+from .evaluate import evaluate
 from .convert import convert
 from .model import model

spacy/cli/evaluate.py

@@ -0,0 +1,93 @@
+# coding: utf8
+from __future__ import unicode_literals, division, print_function
+
+import plac
+import json
+from collections import defaultdict
+import cytoolz
+from pathlib import Path
+import dill
+import tqdm
+from thinc.neural._classes.model import Model
+from thinc.neural.optimizers import linear_decay
+from timeit import default_timer as timer
+import random
+import numpy.random
+
+from ..tokens.doc import Doc
+from ..scorer import Scorer
+from ..gold import GoldParse, merge_sents
+from ..gold import GoldCorpus, minibatch
+from ..util import prints
+from .. import util
+from .. import about
+from .. import displacy
+from ..compat import json_dumps
+
+random.seed(0)
+numpy.random.seed(0)
+
+
+@plac.annotations(
+    model=("Model name or path", "positional", None, str),
+    data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
+    gold_preproc=("Use gold preprocessing", "flag", "G", bool),
+)
+def evaluate(cmd, model, data_path, gold_preproc=False):
+    """
+    Train a model. Expects data in spaCy's JSON format.
+    """
+    util.set_env_log(True)
+    data_path = util.ensure_path(data_path)
+    if not data_path.exists():
+        prints(data_path, title="Evaluation data not found", exits=1)
+    corpus = GoldCorpus(data_path, data_path)
+    nlp = util.load_model(model)
+    scorer = nlp.evaluate(list(corpus.dev_docs(nlp, gold_preproc=gold_preproc)))
+    print_results(scorer)
+
+
+def _render_parses(i, to_render):
+    to_render[0].user_data['title'] = "Batch %d" % i
+    with Path('/tmp/entities.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='ent', page=True)
+        file_.write(html)
+    with Path('/tmp/parses.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='dep', page=True)
+        file_.write(html)
+
+
+def print_progress(itn, losses, dev_scores, wps=0.0):
+    scores = {}
+    for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
+                'ents_p', 'ents_r', 'ents_f', 'wps']:
+        scores[col] = 0.0
+    scores['dep_loss'] = losses.get('parser', 0.0)
+    scores['ner_loss'] = losses.get('ner', 0.0)
+    scores['tag_loss'] = losses.get('tagger', 0.0)
+    scores.update(dev_scores)
+    scores['wps'] = wps
+    tpl = '\t'.join((
+        '{:d}',
+        '{dep_loss:.3f}',
+        '{ner_loss:.3f}',
+        '{uas:.3f}',
+        '{ents_p:.3f}',
+        '{ents_r:.3f}',
+        '{ents_f:.3f}',
+        '{tags_acc:.3f}',
+        '{token_acc:.3f}',
+        '{wps:.1f}'))
+    print(tpl.format(itn, **scores))
+
+
+def print_results(scorer):
+    results = {
+        'TOK': '%.2f' % scorer.token_acc,
+        'POS': '%.2f' % scorer.tags_acc,
+        'UAS': '%.2f' % scorer.uas,
+        'LAS': '%.2f' % scorer.las,
+        'NER P': '%.2f' % scorer.ents_p,
+        'NER R': '%.2f' % scorer.ents_r,
+        'NER F': '%.2f' % scorer.ents_f}
+    util.print_table(results, title="Results")

spacy/cli/package.py

@@ -105,8 +105,11 @@ def generate_pipeline():
            "parser, ner. For more information, see the docs on processing pipelines.",
            title="Enter your model's pipeline components")
     pipeline = util.get_raw_input("Pipeline components", True)
-    replace = {'True': True, 'False': False}
-    return replace[pipeline] if pipeline in replace else pipeline.split(', ')
+    subs = {'True': True, 'False': False}
+    if pipeline in subs:
+        return subs[pipeline]
+    else:
+        return [p.strip() for p in pipeline.split(',')]
 
 
 def validate_meta(meta, keys):

spacy/syntax/nn_parser.pyx

@@ -533,7 +533,7 @@ cdef class Parser:
 
         states, golds, max_steps = self._init_gold_batch(docs, golds)
         (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
-                                                                            0.0)
+                                                                            drop)
         todo = [(s, g) for (s, g) in zip(states, golds)
                 if not s.is_final() and g is not None]
         if not todo:
@@ -598,7 +598,7 @@ cdef class Parser:
             self.moves.preprocess_gold(gold)
 
         cuda_stream = get_cuda_stream()
-        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, 0.0)
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, drop)
 
         states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
                                         states, golds,
@@ -685,7 +685,7 @@ cdef class Parser:
         tok2vec, lower, upper = self.model
         tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
         state2vec = precompute_hiddens(len(docs), tokvecs,
-                        lower, stream, drop=dropout)
+                                       lower, stream, drop=0.0)
         return (tokvecs, bp_tokvecs), state2vec, upper
 
     nr_feature = 8

spacy/util.py

@@ -181,9 +181,10 @@ def is_package(name):
     name (unicode): Name of package.
     RETURNS (bool): True if installed package, False if not.
     """
+    name = name.lower()  # compare package name against lowercase name
     packages = pkg_resources.working_set.by_key.keys()
     for package in packages:
-        if package.replace('-', '_') == name:
+        if package.lower().replace('-', '_') == name:
             return True
     return False
 
@@ -194,6 +195,7 @@ def get_package_path(name):
     name (unicode): Package name.
     RETURNS (Path): Path to installed package.
     """
+    name = name.lower()  # use lowercase version to be safe
     # Here we're importing the module just to find it. This is worryingly
     # indirect, but it's otherwise very difficult to find the package.
     pkg = importlib.import_module(name)

spacy/vocab.pyx

@@ -262,7 +262,7 @@ cdef class Vocab:
         Words can be looked up by string or int ID.
 
         RETURNS:
-            A word vector. Size and shape determed by the
+            A word vector. Size and shape determined by the
             vocab.vectors instance. Usually, a numpy ndarray
             of shape (300,) and dtype float32.
 
@@ -324,6 +324,7 @@ cdef class Vocab:
             self.lexemes_from_bytes(file_.read())
         if self.vectors is not None:
             self.vectors.from_disk(path, exclude='strings.json')
+        link_vectors_to_models(self)
         return self
 
     def to_bytes(self, **exclude):