Merge branch 'master' of https://github.com/explosion/spaCy into feature/better-faster-matcher

requirements.txt

@@ -6,7 +6,6 @@ preshed>=1.0.0,<2.0.0
 thinc>=6.10.1,<6.11.0
 murmurhash>=0.28,<0.29
 plac<1.0.0,>=0.9.6
-six
 ujson>=1.35
 dill>=0.2,<0.3
 requests>=2.13.0,<3.0.0
@@ -16,4 +15,3 @@ pytest>=3.0.6,<4.0.0
 mock>=2.0.0,<3.0.0
 msgpack-python
 msgpack-numpy==0.4.1
-html5lib==1.0b8

setup.py

@@ -191,8 +191,6 @@ def setup_package():
                 'preshed>=1.0.0,<2.0.0',
                 'thinc>=6.10.1,<6.11.0',
                 'plac<1.0.0,>=0.9.6',
-                'six',
-                'html5lib==1.0b8',
                 'pathlib',
                 'ujson>=1.35',
                 'dill>=0.2,<0.3',

spacy/about.py

@@ -3,13 +3,13 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy'
-__version__ = '2.0.8'
+__version__ = '2.1.0.dev0'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'
 __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
-__release__ = True
+__release__ = False
 
 __docs_models__ = 'https://spacy.io/usage/models'
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'

spacy/compat.py

@@ -1,7 +1,6 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-import six
 import ftfy
 import sys
 import ujson
@@ -47,9 +46,10 @@ is_windows = sys.platform.startswith('win')
 is_linux = sys.platform.startswith('linux')
 is_osx = sys.platform == 'darwin'
 
-is_python2 = six.PY2
-is_python3 = six.PY3
-is_python_pre_3_5 = is_python2 or (is_python3 and sys.version_info[1]<5)
+# See: https://github.com/benjaminp/six/blob/master/six.py
+is_python2 = sys.version_info[0] == 2
+is_python3 = sys.version_info[0] == 3
+is_python_pre_3_5 = is_python2 or (is_python3 and sys.version_info[1] < 5)
 
 if is_python2:
     bytes_ = str

spacy/lang/lex_attrs.py

@@ -144,7 +144,7 @@ def is_lower(string): return string.islower()
 def is_space(string): return string.isspace()
 def is_title(string): return string.istitle()
 def is_upper(string): return string.isupper()
-def is_stop(string, stops=set()): return string in stops
+def is_stop(string, stops=set()): return string.lower() in stops
 def is_oov(string): return True
 def get_prob(string): return -20.
 

spacy/matcher.pyx

@@ -637,17 +637,35 @@ cdef class PhraseMatcher:
                 on_match(self, doc, i, matches)
         return matches
 
-    def pipe(self, stream, batch_size=1000, n_threads=2):
+    def pipe(self, stream, batch_size=1000, n_threads=2, return_matches=False,
+             as_tuples=False):
         """Match a stream of documents, yielding them in turn.
 
         docs (iterable): A stream of documents.
         batch_size (int): Number of documents to accumulate into a working set.
         n_threads (int): The number of threads with which to work on the buffer
             in parallel, if the implementation supports multi-threading.
+        return_matches (bool): Yield the match lists along with the docs, making
+            results (doc, matches) tuples.
+        as_tuples (bool): Interpret the input stream as (doc, context) tuples,
+            and yield (result, context) tuples out.
+            If both return_matches and as_tuples are True, the output will
+            be a sequence of ((doc, matches), context) tuples.
         YIELDS (Doc): Documents, in order.
         """
+        if as_tuples:
+            for doc, context in stream:
+                matches = self(doc)
+                if return_matches:
+                    yield ((doc, matches), context)
+                else:
+                    yield (doc, context)
+        else:
             for doc in stream:
-            self(doc)
+                matches = self(doc)
+                if return_matches:
+                    yield (doc, matches) 
+                else:
                     yield doc
 
     def accept_match(self, Doc doc, int start, int end):

spacy/symbols.pxd

@@ -85,6 +85,7 @@ cdef enum symbol_t:
     SENT_START
     SPACY
     PROB
+    LANG
 
     ADJ
     ADP
@@ -108,8 +109,9 @@ cdef enum symbol_t:
     SPACE
 
     Animacy_anim
-    Animacy_inam
+    Animacy_inan
     Animacy_hum # U20
+    Animacy_nhum
     Aspect_freq
     Aspect_imp
     Aspect_mod
@@ -393,6 +395,7 @@ cdef enum symbol_t:
     EVENT
     WORK_OF_ART
     LANGUAGE
+    LAW
 
     DATE
     TIME
@@ -451,10 +454,9 @@ cdef enum symbol_t:
     prt
     punct
     quantmod
+    relcl
     rcmod
     root
     xcomp
 
     acl
-    LAW
-    LANG

spacy/symbols.pyx

@@ -114,8 +114,9 @@ IDS = {
     "SPACE": SPACE,
 
     "Animacy_anim": Animacy_anim,
-    "Animacy_inam": Animacy_inam,
+    "Animacy_inam": Animacy_inan,
     "Animacy_hum": Animacy_hum, # U20
+    "Animacy_nhum": Animacy_nhum,
     "Aspect_freq": Aspect_freq,
     "Aspect_imp": Aspect_imp,
     "Aspect_mod": Aspect_mod,
@@ -458,6 +459,7 @@ IDS = {
     "punct": punct,
     "quantmod": quantmod,
     "rcmod": rcmod,
+    "relcl": relcl,
     "root": root,
     "xcomp": xcomp,
 

spacy/tests/doc/test_span.py

@@ -19,6 +19,15 @@ def doc(en_tokenizer):
     return get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
 
 
+@pytest.fixture
+def doc_not_parsed(en_tokenizer):
+    text = "This is a sentence. This is another sentence. And a third."
+    tokens = en_tokenizer(text)
+    d = get_doc(tokens.vocab, [t.text for t in tokens])
+    d.is_parsed = False
+    return d
+
+
 def test_spans_sent_spans(doc):
     sents = list(doc.sents)
     assert sents[0].start == 0
@@ -34,6 +43,7 @@ def test_spans_root(doc):
     assert span.root.text == 'sentence'
     assert span.root.head.text == 'is'
 
+
 def test_spans_string_fn(doc):
     span = doc[0:4]
     assert len(span) == 4
@@ -41,6 +51,7 @@ def test_spans_string_fn(doc):
     assert span.upper_ == 'THIS IS A SENTENCE'
     assert span.lower_ == 'this is a sentence'
 
+
 def test_spans_root2(en_tokenizer):
     text = "through North and South Carolina"
     heads = [0, 3, -1, -2, -4]
@@ -49,12 +60,17 @@ def test_spans_root2(en_tokenizer):
     assert doc[-2:].root.text == 'Carolina'
 
 
-def test_spans_span_sent(doc):
+def test_spans_span_sent(doc, doc_not_parsed):
     """Test span.sent property"""
     assert len(list(doc.sents))
     assert doc[:2].sent.root.text == 'is'
     assert doc[:2].sent.text == 'This is a sentence .'
     assert doc[6:7].sent.root.left_edge.text == 'This'
+    # test on manual sbd
+    doc_not_parsed[0].is_sent_start = True
+    doc_not_parsed[5].is_sent_start = True
+    assert doc_not_parsed[1:3].sent == doc_not_parsed[0:5]
+    assert doc_not_parsed[10:14].sent == doc_not_parsed[5:]
 
 
 def test_spans_lca_matrix(en_tokenizer):

spacy/tests/lang/en/test_tagger.py

@@ -2,9 +2,9 @@
 from __future__ import unicode_literals
 
 from ....parts_of_speech import SPACE
+from ....compat import unicode_
 from ...util import get_doc
 
-import six
 import pytest
 
 
@@ -24,8 +24,8 @@ def test_tag_names(EN):
     text = "I ate pizzas with anchovies."
     doc = EN(text, disable=['parser'])
     assert type(doc[2].pos) == int
-    assert isinstance(doc[2].pos_, six.text_type)
-    assert isinstance(doc[2].dep_, six.text_type)
+    assert isinstance(doc[2].pos_, unicode_)
+    assert isinstance(doc[2].dep_, unicode_)
     assert doc[2].tag_ == u'NNS'
 
 

spacy/tests/regression/test_issue1889.py

@@ -0,0 +1,11 @@
+# coding: utf-8
+from __future__ import unicode_literals
+from ...lang.lex_attrs import is_stop
+from ...lang.en.stop_words import STOP_WORDS
+
+import pytest
+
+
+@pytest.mark.parametrize('word', ['the'])
+def test_lex_attrs_stop_words_case_sensitivity(word):
+    assert is_stop(word, STOP_WORDS) == is_stop(word.upper(), STOP_WORDS)

spacy/tokens/doc.pyx

@@ -186,6 +186,20 @@ cdef class Doc:
     def _(self):
         return Underscore(Underscore.doc_extensions, self)
 
+    @property
+    def is_sentenced(self):
+        # Check if the document has sentence boundaries,
+        # i.e at least one tok has the sent_start in (-1, 1)
+        if 'sents' in self.user_hooks:
+            return True
+        if self.is_parsed:
+            return True
+        for i in range(self.length):
+            if self.c[i].sent_start == -1 or self.c[i].sent_start == 1:
+                return True
+        else:
+            return False
+
     def __getitem__(self, object i):
         """Get a `Token` or `Span` object.
 
@@ -515,22 +529,16 @@ cdef class Doc:
             >>> assert [s.root.text for s in doc.sents] == ["is", "'s"]
         """
         def __get__(self):
-            if 'sents' in self.user_hooks:
-                yield from self.user_hooks['sents'](self)
-                return
-
-            cdef int i
-            if not self.is_parsed:
-                for i in range(1, self.length):
-                    if self.c[i].sent_start != 0:
-                        break
-                else:
+            if not self.is_sentenced:
                 raise ValueError(
                     "Sentence boundaries unset. You can add the 'sentencizer' "
                     "component to the pipeline with: "
                     "nlp.add_pipe(nlp.create_pipe('sentencizer')) "
                     "Alternatively, add the dependency parser, or set "
                     "sentence boundaries by setting doc[i].sent_start")
+            if 'sents' in self.user_hooks:
+                yield from self.user_hooks['sents'](self)
+            else:
                 start = 0
                 for i in range(1, self.length):
                     if self.c[i].sent_start == 1:

spacy/tokens/span.pyx

@@ -285,9 +285,14 @@ cdef class Span:
         def __get__(self):
             if 'sent' in self.doc.user_span_hooks:
                 return self.doc.user_span_hooks['sent'](self)
-            # This should raise if we're not parsed.
+            # This should raise if we're not parsed
+            # or doesen't have any sbd component :)
             self.doc.sents
+            # if doc is parsed we can use the deps to find the sentence
+            # otherwise we use the `sent_start` token attribute
             cdef int n = 0
+            cdef int i
+            if self.doc.is_parsed:
                 root = &self.doc.c[self.start]
                 while root.head != 0:
                     root += root.head
@@ -295,6 +300,24 @@ cdef class Span:
                     if n >= self.doc.length:
                         raise RuntimeError
                 return self.doc[root.l_edge:root.r_edge + 1]
+            elif self.doc.is_sentenced:
+                # find start of the sentence
+                start = self.start
+                while self.doc.c[start].sent_start != 1 and start > 0:
+                    start += -1
+                # find end of the sentence
+                end = self.end
+                while self.doc.c[end].sent_start != 1:
+                    end += 1
+                    if n >= self.doc.length:
+                        break
+                #
+                return self.doc[start:end]
+            else:
+                raise ValueError(
+                    "Access to sentence requires either the dependency parse "
+                    "or sentence boundaries to be set by setting " +
+                    "doc[i].is_sent_start = True")
 
     property has_vector:
         """RETURNS (bool): Whether a word vector is associated with the object.

website/api/lexeme.jade

@@ -325,6 +325,12 @@ p The L2 norm of the lexeme's vector representation.
         +cell bool
         +cell Is the lexeme a quotation mark?
 
+    +row
+        +cell #[code is_currency]
+            +tag-new("2.0.8")
+        +cell bool
+        +cell Is the lexeme a currency symbol?
+
     +row
         +cell #[code like_url]
         +cell bool

website/api/matcher.jade

@@ -111,6 +111,25 @@ p Match a stream of documents, yielding them in turn.
             |  parallel, if the #[code Matcher] implementation supports
             |  multi-threading.
 
+    +row
+        +cell #[code return_matches]
+            +tag-new(2.1)
+        +cell bool
+        +cell
+            |  Yield the match lists along with the docs, making results
+            |  #[code (doc, matches)] tuples.
+
+    +row
+        +cell #[code as_tuples]
+            +tag-new(2.1)
+        +cell bool
+        +cell
+            |  Interpret the input stream as #[code (doc, context)] tuples, and
+            |  yield #[code (result, context)] tuples out. If both
+            |  #[code return_matches] and #[code as_tuples] are #[code True],
+            |  the output will be a sequence of
+            |  #[code ((doc, matches), context)] tuples.
+
     +row("foot")
         +cell yields
         +cell #[code Doc]

website/api/token.jade

@@ -740,6 +740,12 @@ p The L2 norm of the token's vector representation.
         +cell bool
         +cell Is the token a quotation mark?
 
+    +row
+        +cell #[code is_currency]
+            +tag-new("2.0.8")
+        +cell bool
+        +cell Is the token a currency symbol?
+
     +row
         +cell #[code like_url]
         +cell bool