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* Upd docstrings

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This commit is contained in:
Matthew Honnibal 2014-12-27 18:45:16 +11:00
parent 91a5064b7f
commit bb80937544
5 changed files with 272 additions and 29 deletions
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57
spacy/en/__init__.py
View File

@ -16,16 +16,64 @@ def get_lex_props(string):
class English(object):
def __init__(self, data_dir=None, tag=True, parse=False):
"""The English NLP pipeline.
Provides a tokenizer, lexicon, part-of-speech tagger and parser.
Keyword args:
data_dir (unicode): A path to a directory, from which to load the pipeline.
If None, looks for a directory named "data/" in the same directory as
the present file, i.e. path.join(path.dirname(__file__, 'data')).
If path.join(data_dir, 'pos') exists, the tagger is loaded from it.
If path.join(data_dir, 'deps') exists, the parser is loaded from it.
See Pipeline Directory Structure for details.
Attributes:
vocab (spacy.vocab.Vocab): The lexicon.
strings (spacy.strings.StringStore): Encode/decode strings to/from integer IDs.
tokenizer (spacy.tokenizer.Tokenizer): The start of the pipeline.
tagger (spacy.en.pos.EnPosTagger):
The part-of-speech tagger, which also performs lemmatization and
morphological analysis.
parser (spacy.syntax.parser.GreedyParser):
A greedy shift-reduce dependency parser.
"""
def __init__(self, data_dir=None):
if data_dir is None:
data_dir = path.join(path.dirname(__file__), 'data')
self.vocab = Vocab(data_dir=data_dir, get_lex_props=get_lex_props)
self.tokenizer = Tokenizer.from_dir(self.vocab, data_dir)
self.tagger = EnPosTagger(self.vocab.strings, data_dir) if tag else None
self.parser = GreedyParser(path.join(data_dir, 'deps')) if parse else None
if path.exists(path.join(data_dir, 'pos')):
self.tagger = EnPosTagger(self.vocab.strings, data_dir)
else:
self.tagger = None
if path.exists(path.join(data_dir, 'deps')):
self.parser = GreedyParser(path.join(data_dir, 'deps'))
else:
self.parser = None
self.strings = self.vocab.strings
def __call__(self, text, tag=True, parse=True):
"""Apply the pipeline to some text.
Args:
text (unicode): The text to be processed.
Keyword args:
tag (bool): Whether to add part-of-speech tags to the text. This
will also set morphological analysis and lemmas.
parse (bool): Whether to add dependency-heads and labels to the text.
Returns:
tokens (spacy.tokens.Tokens):
"""
tokens = self.tokenizer.tokenize(text)
if self.tagger and tag:
self.tagger(tokens)
@ -35,7 +83,10 @@ class English(object):
@property
def tags(self):
"""List of part-of-speech tag names."""
if self.tagger is None:
return []
else:
return self.tagger.tag_names

6
spacy/en/pos.pyx
View File

@ -204,6 +204,7 @@ cdef struct _CachedMorph:
cdef class EnPosTagger(Tagger):
"""A part-of-speech tagger for English"""
def __init__(self, StringStore strings, data_dir):
model_dir = path.join(data_dir, 'pos')
Tagger.__init__(self, path.join(model_dir))
@ -224,6 +225,11 @@ cdef class EnPosTagger(Tagger):
self.lemmatizer = Lemmatizer(path.join(data_dir, 'wordnet'), NOUN, VERB, ADJ)
def __call__(self, Tokens tokens):
"""Apply the tagger, setting the POS tags onto the Tokens object.
Args:
tokens (Tokens): The tokens to be tagged.
"""
cdef int i
cdef atom_t[N_CONTEXT_FIELDS] context
cdef TokenC* t = tokens.data

162
spacy/syntax/_parse_features.pyx
View File

@ -13,7 +13,8 @@ from itertools import combinations
from ..tokens cimport TokenC
from ._state cimport State
from ._state cimport get_s2, get_s1, get_s0, get_n0, get_n1, get_n2
from ._state cimport get_left, get_right
from ._state cimport has_head, get_left, get_right
from ._state cimport count_left_kids, count_right_kids
cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
@ -24,10 +25,12 @@ cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
context[3] = 0
context[4] = 0
context[5] = 0
context[6] = 0
else:
context[0] = token.lex.sic
context[1] = token.pos
context[2] = token.lex.cluster
context[1] = token.lemma
context[2] = token.fine_pos
context[3] = token.lex.cluster
# We've read in the string little-endian, so now we can take & (2**n)-1
# to get the first n bits of the cluster.
# e.g. s = "1110010101"
@ -40,9 +43,9 @@ cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
# What we're doing here is picking a number where all bits are 1, e.g.
# 15 is 1111, 63 is 111111 and doing bitwise AND, so getting all bits in
# the source that are set to 1.
context[3] = token.lex.cluster & 63
context[4] = token.lex.cluster & 15
context[5] = token.dep_tag
context[4] = token.lex.cluster & 63
context[5] = token.lex.cluster & 15
context[6] = token.dep_tag if has_head(token) else 0
cdef int fill_context(atom_t* context, State* state) except -1:
@ -66,11 +69,147 @@ cdef int fill_context(atom_t* context, State* state) except -1:
context[dist] = state.stack[0] - state.i
else:
context[dist] = 0
context[N0lv] = 0
context[S0lv] = 0
context[S0rv] = 0
context[S1lv] = 0
context[S1rv] = 0
context[N0lv] = max(count_left_kids(get_n0(state)), 5)
context[S0lv] = max(count_left_kids(get_s0(state)), 5)
context[S0rv] = max(count_right_kids(get_s0(state)), 5)
context[S1lv] = max(count_left_kids(get_s1(state)), 5)
context[S1rv] = max(count_right_kids(get_s1(state)), 5)
context[S0_has_head] = 0
context[S1_has_head] = 0
context[S2_has_head] = 0
if state.stack_len >= 1:
context[S0_has_head] = has_head(get_s0(state)) + 1
if state.stack_len >= 2:
context[S1_has_head] = has_head(get_s1(state)) + 1
if state.stack_len >= 3:
context[S2_has_head] = has_head(get_s2(state))
unigrams = (
(S2W, S2p),
(S2c6, S2p),
(S1W, S1p),
(S1c6, S1p),
(S0W, S0p),
(S0c6, S0p),
(N0W, N0p),
(N0p,),
(N0c,),
(N0c6, N0p),
(N0L,),
(N1W, N1p),
(N1c6, N1p),
(N2W, N2p),
(N2c6, N2p),
(S0r2W, S0r2p),
(S0r2c6, S0r2p),
(S0r2L,),
(S0rW, S0rp),
(S0rc6, S0rp),
(S0rL,),
(S0l2W, S0l2p),
(S0l2c6, S0l2p),
(S0l2L,),
(S0lW, S0lp),
(S0lc6, S0lp),
(S0lL,),
(N0l2W, N0l2p),
(N0l2c6, N0l2p),
(N0l2L,),
(N0lW, N0lp),
(N0lc6, N0lp),
(N0lL,),
)
s0_n0 = (
(S0W, S0p, N0W, N0p),
(S0c, S0p, N0c, N0p),
(S0c6, S0p, N0c6, N0p),
(S0c4, S0p, N0c4, N0p),
(S0p, N0p),
(S0W, N0p),
(S0p, N0W),
(S0W, N0c),
(S0c, N0W),
(S0p, N0c),
(S0c, N0p),
(S0W, S0rp, N0p),
(S0p, S0rp, N0p),
(S0p, N0lp, N0W),
(S0p, N0lp, N0p),
)
s1_n0 = (
(S1p, N0p),
(S1c, N0c),
(S1c, N0p),
(S1p, N0c),
(S1W, S1p, N0p),
(S1p, N0W, N0p),
(S1c6, S1p, N0c6, N0p),
)
s0_n1 = (
(S0p, N1p),
(S0c, N1c),
(S0c, N1p),
(S0p, N1c),
(S0W, S0p, N1p),
(S0p, N1W, N1p),
(S0c6, S0p, N1c6, N1p),
)
n0_n1 = (
(N0W, N0p, N1W, N1p),
(N0W, N0p, N1p),
(N0p, N1W, N1p),
(N0c, N0p, N1c, N1p),
(N0c6, N0p, N1c6, N1p),
(N0c, N1c),
(N0p, N1c),
)
tree_shape = (
(dist,),
(S0p, S0_has_head, S1_has_head, S2_has_head),
(S0p, S0lv, S0rv),
(N0p, N0lv),
)
trigrams = (
(N0p, N1p, N2p),
(S0p, S0lp, S0l2p),
(S0p, S0rp, S0r2p),
(S0p, S1p, S2p),
(S1p, S0p, N0p),
(S0p, S0lp, N0p),
(S0p, N0p, N0lp),
(N0p, N0lp, N0l2p),
(S0W, S0p, S0rL, S0r2L),
(S0p, S0rL, S0r2L),
(S0W, S0p, S0lL, S0l2L),
(S0p, S0lL, S0l2L),
(N0W, N0p, N0lL, N0l2L),
(N0p, N0lL, N0l2L),
)
arc_eager = (
@ -86,7 +225,6 @@ arc_eager = (
(N2w, N2p),
(N2w,),
(N2p,),
(S0w, S0p, N0w, N0p),
(S0w, S0p, N0w),
(S0w, N0w, N0p),

73
spacy/tokens.pyx
View File

@ -1,4 +1,5 @@
# cython: profile=True
from preshed.maps cimport PreshMap
from preshed.counter cimport PreshCounter
@ -59,13 +60,7 @@ cdef attr_t get_lex_attr(const Lexeme* lex, attr_id_t feat_name) nogil:
cdef class Tokens:
"""A sequence of references to Lexeme objects.
The Tokens class provides fast and memory-efficient access to lexical features,
and can efficiently export the data to a numpy array.
>>> from spacy.en import EN
>>> tokens = EN.tokenize('An example sentence.')
"""Access and set annotations onto some text.
"""
def __init__(self, Vocab vocab, string_length=0):
self.vocab = vocab
@ -86,10 +81,20 @@ cdef class Tokens:
self.length = 0
def __getitem__(self, i):
"""Retrieve a token.
Returns:
token (Token):
"""
bounds_check(i, self.length, PADDING)
return Token(self, i)
def __iter__(self):
"""Iterate over the tokens.
Yields:
token (Token):
"""
for i in range(self.length):
yield self[i]
@ -148,6 +153,11 @@ cdef class Tokens:
@cython.freelist(64)
cdef class Token:
"""An individual token.
Internally, the Token is a tuple (i, tokens) --- it delegates to the Tokens
object.
"""
def __init__(self, Tokens tokens, int i):
self._seq = tokens
self.i = i
@ -163,21 +173,44 @@ cdef class Token:
return self.string + ' '
def __len__(self):
"""The number of unicode code-points in the original string.
Returns:
length (int):
"""
return self._seq.data[self.i].lex.length
property idx:
"""The index into the original string at which the token starts.
The following is supposed to always be true:
>>> original_string[token.idx:token.idx len(token) == token.string
"""
def __get__(self):
return self._seq.data[self.i].idx
property length:
def __get__(self):
return self._seq.data[self.i].lex.length
property cluster:
"""The Brown cluster ID of the word: en.wikipedia.org/wiki/Brown_clustering
Similar words have better-than-chance likelihood of having similar cluster
IDs, although the clustering is quite noisy. Cluster IDs make good features,
and help to make models slightly more robust to domain variation.
A common trick is to use only the first N bits of a cluster ID in a feature,
as the more general part of the hierarchical clustering is often more accurate
than the lower categories.
To assist in this, I encode the cluster IDs little-endian, to allow a simple
bit-mask:
>>> six_bits = cluster & (2**6 - 1)
"""
def __get__(self):
return self._seq.data[self.i].lex.cluster
property string:
"""The unicode string of the word, with no whitespace padding."""
def __get__(self):
cdef const TokenC* t = &self._seq.data[self.i]
if t.lex.sic == 0:
@ -186,6 +219,9 @@ cdef class Token:
return utf8string.decode('utf8')
property lemma:
"""The unicode string of the word's lemma. If no part-of-speech tag is
assigned, the most common part-of-speech tag of the word is used.
"""
def __get__(self):
cdef const TokenC* t = &self._seq.data[self.i]
if t.lemma == 0:
@ -193,15 +229,27 @@ cdef class Token:
cdef bytes utf8string = self._seq.vocab.strings[t.lemma]
return utf8string.decode('utf8')
property dep:
property dep_tag:
"""The ID integer of the word's dependency label. If no parse has been
assigned, defaults to 0.
"""
def __get__(self):
return self._seq.data[self.i].dep_tag
property pos:
"""The ID integer of the word's part-of-speech tag, from the 13-tag
Google Universal Tag Set. Constants for this tag set are available in
spacy.typedefs.
"""
def __get__(self):
return self._seq.data[self.i].pos
property fine_pos:
"""The ID integer of the word's fine-grained part-of-speech tag, as assigned
by the tagger model. Fine-grained tags include morphological information,
and other distinctions, and allow a more accurate tagger to be trained.
"""
def __get__(self):
return self._seq.data[self.i].fine_pos
@ -210,6 +258,7 @@ cdef class Token:
return self._seq.data[self.i].lex.sic
property head:
"""The token predicted by the parser to be the head of the current token."""
def __get__(self):
cdef const TokenC* t = &self._seq.data[self.i]
return Token(self._seq, self.i + t.head)

3
spacy/vocab.pyx
View File

@ -33,8 +33,6 @@ cpdef Lexeme init_lexeme(id_t i, unicode string, hash_t hashed,
cdef class Vocab:
'''A map container for a language's Lexeme structs.
Also interns UTF-8 strings, and maps them to consecutive integer IDs.
'''
def __init__(self, data_dir=None, get_lex_props=None):
self.mem = Pool()
@ -53,6 +51,7 @@ cdef class Vocab:
self.load(path.join(data_dir, 'lexemes'))
def __len__(self):
"""The current number of lexemes stored."""
return self.lexemes.size()
cdef const Lexeme* get(self, Pool mem, UniStr* string) except NULL: