Merge branch 'master' of ssh://github.com/honnibal/spaCy

.gitignore

@@ -17,6 +17,7 @@ models/
 spacy/syntax/*.cpp
 spacy/syntax/*.html
 spacy/en/*.cpp
+spacy/en/data/*
 spacy/*.cpp
 spacy/ner/*.cpp
 spacy/orthography/*.cpp

.travis.yml

@@ -8,12 +8,12 @@ python:
   - "2.7"
   - "3.4"
 
-# command to install dependencies
+# install dependencies
 install:
   - "pip install --upgrade setuptools"
   - "pip install -r requirements.txt"
   - "export PYTHONPATH=`pwd`"
   - "python setup.py build_ext --inplace"
-# command to run tests
+# run tests
 script:
   - py.test tests/

README.md

@@ -3,20 +3,18 @@ spaCy
 
 http://honnibal.github.io/spaCy
 
-Fast, state-of-the-art natural language processing pipeline. Commercial licenses available, or use under AGPL.
+A pipeline for fast, state-of-the-art natural language processing. Commercial licenses available, otherwise under AGPL.
 
 Version 0.80 released
 ---------------------
 
 2015-04-13
 
-* Preliminary named entity recognition support. Accuracy is currently
-  substantially behind the current state-of-the-art. I'm working on
-  improvements. 
+* Preliminary support for named-entity recognition. Its accuracy is substantially behind the state-of-the-art. I'm working on improvements. 
 
 * Better sentence boundary detection, drawn from the syntactic structure.
 
-* Lots of bug fixes
+* Lots of bug fixes.
 
 
 Supports:
@@ -35,4 +33,3 @@ Difficult to support:
 
 * PyPy 2.7
 * PyPy 3.4
-

bin/ner_tag.py

@@ -30,5 +30,3 @@ def main(text_loc):
 
 if __name__ == '__main__':
     plac.call(main)
-
-        

bin/parser/train.py

@@ -7,8 +7,6 @@ from os import path
 import shutil
 import codecs
 import random
-import time
-import gzip
 
 import plac
 import cProfile

bin/prepare_vecs.py

@@ -1,8 +1,6 @@
 """Read a vector file, and prepare it as binary data, for easy consumption"""
 
-import bz2
 import plac
-import struct
 
 from spacy.vocab import write_binary_vectors
 

contributors/cla.md

@@ -1,7 +1,7 @@
 Signing the Contributors License Agreement
 ==========================================
 
-SpaCy is a commercial open-source project, owned by Syllogism Co.  We require that contributors to SpaCy sign our Contributors License Agreement, which is based on the Oracle Contributor Agreement.
+SpaCy is a commercial open-source project, owned by Syllogism Co. We require that contributors to SpaCy sign our Contributors License Agreement, which is based on the Oracle Contributor Agreement.
 
 The CLA must be signed on your first pull request. To do this, simply fill in the file cla_template.md, and include the filed in form in your first pull request.
 
@@ -11,5 +11,3 @@ The CLA must be signed on your first pull request. To do this, simply fill in th
     $ git add -A spaCy/contributors/<your GitHub username>.md
     
 Now finish your pull request, and you're done.
-
-

contributors/cla_template.md

@@ -2,7 +2,7 @@ Syllogism Contributor Agreement
 ===============================
 
 This Syllogism Contributor Agreement (“SCA”) is based on the Oracle Contributor
-Agreement.  The SCA applies to any contribution that you make to any product or
+Agreement. The SCA applies to any contribution that you make to any product or
 project managed by us (the “project”), and sets out the intellectual property
 rights you grant to us in the contributed materials. The term “us” shall mean
 Syllogism Co. The term "you" shall mean the person or entity identified below.

contributors/suchow.md

@@ -0,0 +1,95 @@
+Syllogism Contributor Agreement
+===============================
+
+This Syllogism Contributor Agreement (“SCA”) is based on the Oracle Contributor
+Agreement. The SCA applies to any contribution that you make to any product or
+project managed by us (the “project”), and sets out the intellectual property
+rights you grant to us in the contributed materials. The term “us” shall mean
+Syllogism Co. The term "you" shall mean the person or entity identified below.
+If you agree to be bound by these terms, fill in the information requested below
+and include the filled-in version with your first pull-request, under the file
+contrbutors/. The name of the file should be your GitHub username, with the
+extension .md. For example, the user example_user would create the file
+spaCy/contributors/example_user.md .
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+1. The term 'contribution' or ‘contributed materials’ means any source code,
+object code, patch, tool, sample, graphic, specification, manual, documentation,
+or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and registrations,
+in your contribution:
+  * you hereby assign to us joint ownership, and to the extent that such assignment
+  is or becomes invalid, ineffective or unenforceable, you hereby grant to us a perpetual,
+  irrevocable, non-exclusive, worldwide, no-charge, royalty-free, unrestricted license
+  to exercise all rights under those copyrights. This includes, at our option, the
+  right to sublicense these same rights to third parties through multiple levels of
+  sublicensees or other licensing arrangements;
+
+  * you agree that each of us can do all things in relation to your contribution
+  as if each of us were the sole owners, and if one of us makes a derivative work
+  of your contribution, the one who makes the derivative work (or has it made) will
+  be the sole owner of that derivative work;
+
+  * you agree that you will not assert any moral rights in your contribution against
+  us, our licensees or transferees;
+  
+  * you agree that we may register a copyright in your contribution and exercise
+  all ownership rights associated with it; and
+
+  * you agree that neither of us has any duty to consult with, obtain the consent
+  of, pay or render an accounting to the other for any use or distribution of your
+  contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable, non-exclusive,
+worldwide, no-charge, royalty-free license to:
+
+  * make, have made, use, sell, offer to sell, import, and otherwise transfer your
+  contribution in whole or in part, alone or in combination with
+  or included in any product, work or materials arising out of the project to
+  which your contribution was submitted, and
+
+  * at our option, to sublicense these same rights to third parties through multiple
+  levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective on
+the date you first submitted a contribution to us, even if your submission took
+place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+  * Each contribution that you submit is and shall be an original work of authorship
+  and you can legally grant the rights set out in this SCA;
+  
+  * to the best of your knowledge, each contribution will not violate any third
+  party's copyrights, trademarks, patents, or other intellectual property rights; and
+
+  * each contribution shall be in compliance with U.S. export control laws and other
+  applicable export and import laws. You agree to notify us if you become aware of
+  any circumstance which would make any of the foregoing representations inaccurate
+  in any respect. Syllogism Co. may publicly disclose your participation in the project,
+  including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable U.S.
+  Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+x___ I am signing on behalf of myself as an individual and no other person or entity, including my employer, has or will have rights with respect my contributions.
+
+____ I am signing on behalf of my employer or a legal entity and I have the actual authority to contractually bind that entity.
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Jordan Suchow        |
+| Company's name (if applicable) |                      |
+| Title or Role (if applicable)  |                      |
+| Date                           | 2015-04-19           |
+| GitHub username                | suchow               |
+| Website (optional)             | http://suchow.io     |
+

dev_setup.py

@@ -64,8 +64,6 @@ def clean(ext):
             if os.path.exists(html):
                 os.unlink(html)
 
-
-
 HERE = os.path.dirname(__file__)
 virtual_env = os.environ.get('VIRTUAL_ENV', '')
 compile_args = []

docs/source/api.rst

@@ -107,7 +107,7 @@ API
     *derivational* suffixes are not stripped, e.g. the lemma of "instutitions"
     is "institution", not "institute".  Lemmatization is performed using the
     WordNet data, but extended to also cover closed-class words such as
-    pronouns. By default, the WN lemmatizer returns "hi" as the lemma of "his".
+    pronouns.  By default, the WN lemmatizer returns "hi" as the lemma of "his".
     We assign pronouns the lemma -PRON-.
 
   lower
@@ -121,7 +121,7 @@ API
     A transform of the word's string, to show orthographic features.  The
     characters a-z are mapped to x, A-Z is mapped to X, 0-9 is mapped to d.
     After these mappings, sequences of 4 or more of the same character are
-    truncated to length 4. Examples: C3Po --> XdXx, favorite --> xxxx,
+    truncated to length 4.  Examples: C3Po --> XdXx, favorite --> xxxx,
     :) --> :)
 
   prefix

docs/source/features.rst

@@ -66,7 +66,7 @@ Boolean features
 +-------------+--------------------------------------------------------------+
 | IS_UPPER    | The result of sic.isupper()                                  |
 +-------------+--------------------------------------------------------------+
-| LIKE_URL    | Check whether the string looks like it could be a URL. Aims  |
+| LIKE_URL    | Check whether the string looks like it could be a URL.  Aims |
 |             | for low false negative rate.                                 |
 +-------------+--------------------------------------------------------------+
 | LIKE_NUMBER | Check whether the string looks like it could be a numeric    |
@@ -75,4 +75,3 @@ Boolean features
 +-------------+--------------------------------------------------------------+
 | IN_LIST     | Facility for loading arbitrary run-time word lists?          |
 +-------------+--------------------------------------------------------------+
-

docs/source/guide/overview.rst

@@ -6,7 +6,7 @@ What and Why
 
 spaCy is a lightning-fast, full-cream NLP tokenizer and lexicon.
 
-Most tokenizers give you a sequence of strings. That's barbaric.
+Most tokenizers give you a sequence of strings.  That's barbaric.
 Giving you strings invites you to compute on every *token*, when what
 you should be doing is computing on every *type*.  Remember
 `Zipf's law <http://en.wikipedia.org/wiki/Zipf's_law>`_: you'll
@@ -28,14 +28,14 @@ can access an excellent set of pre-computed orthographic and distributional feat
     >>> are.check_flag(en.CAN_NOUN)
     False
 
-spaCy makes it easy to write very efficient NLP applications, because your feature
+spaCy makes it easy to write efficient NLP applications, because your feature
 functions have to do almost no work: almost every lexical property you'll want
 is pre-computed for you.  See the tutorial for an example POS tagger.
 
 Benchmark
 ---------
 
-The tokenizer itself is also very efficient:
+The tokenizer itself is also efficient:
 
 +--------+-------+--------------+--------------+
 | System | Time	 | Words/second | Speed Factor |
@@ -56,7 +56,7 @@ Pros:
 
 - All tokens come with indices into the original string
 - Full unicode support
-- Extensible to other languages
+- Extendable to other languages
 - Batch operations computed efficiently in Cython
 - Cython API
 - numpy interoperability
@@ -68,4 +68,3 @@ Cons:
 - Higher memory usage (up to 1gb)
 - More conceptually complicated
 - Tokenization rules expressed in code, not as data
-

docs/source/howworks.rst

@@ -116,7 +116,7 @@ this was written quickly and has not been executed):
 
 
 This procedure splits off tokens from the start and end of the string, at each
-point checking whether the remaining string is in our special-cases table. If
+point checking whether the remaining string is in our special-cases table.  If
 it is, we stop splitting, and return the tokenization at that point.
 
 The advantage of this design is that the prefixes, suffixes and special-cases
@@ -135,7 +135,7 @@ lexical types.
 
 In a sample of text, vocabulary size grows exponentially slower than word
 count.  So any computations we can perform over the vocabulary and apply to the
-word count are very efficient.
+word count are efficient.
 
 
 Part-of-speech Tagger
@@ -206,8 +206,8 @@ loop:
             class_, score = max(enumerate(scores), key=lambda item: item[1])
             transition(state, class_)
 
-The parser makes 2N transitions for a sentence of length N. In order to select
-the transition, it extracts a vector of K features from the state. Each feature
+The parser makes 2N transitions for a sentence of length N.  In order to select
+the transition, it extracts a vector of K features from the state.  Each feature
 is used as a key into a hash table managed by the model.  The features map to
 a vector of weights, of length C.  We then dot product the feature weights to the
 scores vector we are building for that instance.
@@ -260,5 +260,3 @@ these models is really all about the data structures.  We want to stay small,
 and stay contiguous.  Minimize redundancy and minimize pointer chasing.
 That's why Cython is so well suited to this: we get to lay out our data
 structures, and manage the memory ourselves, with full C-level control.
-
-

docs/source/index.rst

@@ -10,7 +10,7 @@ spaCy: Industrial-strength NLP
 
 .. _Issue Tracker: https://github.com/honnibal/spaCy/issues
 
-**13/04**: *Version 0.80 released. Includes named entity recognition, better sentence
+**13/04**: *Version 0.80 released.  Includes named entity recognition, better sentence
 boundary detection, and many bug fixes.*
 
 `spaCy`_ is a new library for text processing in Python and Cython.
@@ -28,7 +28,7 @@ If they don't want to stay in academia, they join Google, IBM, etc.
 
 The net result is that outside of the tech giants, commercial NLP has changed
 little in the last ten years.  In academia, it's changed entirely.  Amazing
-improvements in quality. Orders of magnitude faster.  But the
+improvements in quality.  Orders of magnitude faster.  But the
 academic code is always GPL, undocumented, unuseable, or all three.  You could
 implement the ideas yourself, but the papers are hard to read, and training
 data is exorbitantly expensive.  So what are you left with?  A common answer is
@@ -37,7 +37,7 @@ tokenizer is suitable for production use.
 
 I used to think that the NLP community just needed to do more to communicate
 its findings to software engineers.  So I wrote two blog posts, explaining
-`how to write a part-of-speech tagger`_ and `parser`_.  Both were very well received,
+`how to write a part-of-speech tagger`_ and `parser`_.  Both were well received,
 and there's been a bit of interest in `my research software`_ --- even though
 it's entirely undocumented, and mostly unuseable to anyone but me.
 
@@ -58,7 +58,7 @@ to embedded word representations, and a range of useful features are pre-calcula
 and cached.
 
 If none of that made any sense to you, here's the gist of it.  Computers don't
-understand text. This is unfortunate, because that's what the web almost entirely
+understand text.  This is unfortunate, because that's what the web almost entirely
 consists of.  We want to recommend people text based on other text they liked.
 We want to shorten text to display it on a mobile screen.  We want to aggregate
 it, link it, filter it, categorise it, generate it and correct it.
@@ -202,7 +202,7 @@ this:
 
 We wanted to refine the logic so that only adverbs modifying evocative verbs
 of communication, like "pleaded", were highlighted.  We've now built a vector that
-represents that type of word, so now we can highlight adverbs based on very
+represents that type of word, so now we can highlight adverbs based on
 subtle logic, honing in on adverbs that seem the most stylistically
 problematic, given our starting assumptions:
 
@@ -242,7 +242,7 @@ I report mean times per document, in milliseconds.
 
 **Hardware**: Intel i7-3770 (2012)
 
-.. table:: Efficiency comparison. Lower is better. 
+.. table:: Efficiency comparison.  Lower is better.
 
   +--------------+---------------------------+--------------------------------+
   |              | Absolute (ms per doc)     | Relative (to spaCy)            |
@@ -278,7 +278,7 @@ representations.
   publish or perform any benchmark or performance tests or analysis relating to
   the Service or the use thereof without express authorization from AlchemyAPI;
 
-.. Did you get that? You're not allowed to evaluate how well their system works,
+.. Did you get that?  You're not allowed to evaluate how well their system works,
   unless you're granted a special exception.  Their system must be pretty
   terrible to motivate such an embarrassing restriction.
   They must know this makes them look bad, but they apparently believe allowing

docs/source/lexrank_tutorial.rst

@@ -278,6 +278,3 @@ sentence represents the document as a whole.
 
 Document Model
 --------------
-
-
-

docs/source/license.rst

@@ -35,7 +35,7 @@ And if you're ever in acquisition or IPO talks, the story is simple.
 spaCy can also be used as free open-source software, under the Aferro GPL
 license.  If you use it this way, you must comply with the AGPL license terms.
 When you distribute your project, or offer it as a network service, you must
-distribute the source-code, and grant users an AGPL license to it.
+distribute the source-code and grant users an AGPL license to it.
 
 
 .. I left academia in June 2014, just when I should have been submitting my first
@@ -92,7 +92,7 @@ developing.  They own the copyright to any modifications they make to spaCy,
 but not to the original spaCy code.
 
 No additional fees will be due when they hire new developers, run spaCy on
-additional internal servers, etc. If their company is acquired, the license will
+additional internal servers, etc.  If their company is acquired, the license will
 be transferred to the company acquiring them.  However, to use spaCy in another
 product, they will have to buy a second license.
 
@@ -115,9 +115,9 @@ In order to do this, they must sign a contributor agreement, ceding their
 copyright.  When commercial licenses to spaCy are sold, Alex and Sasha will
 not be able to claim any royalties from their contributions.
 
-Later, Alex and Sasha implement new features into spaCy, for another paper. The
+Later, Alex and Sasha implement new features into spaCy, for another paper.  The
 code was quite rushed, and they don't want to take the time to put together a
-proper pull request. They must release their modifications under the AGPL, but
+proper pull request.  They must release their modifications under the AGPL, but
 they are not obliged to contribute it to the spaCy repository, or concede their
 copyright.
 
@@ -126,8 +126,8 @@ Phuong and Jessie: Open Source developers
 #########################################
 
 Phuong and Jessie use the open-source software Calibre to manage their e-book
-libraries. They have an idea for a search feature, and they want to use spaCy
-to implement it. Calibre is released under the GPLv3. The AGPL has additional
+libraries.  They have an idea for a search feature, and they want to use spaCy
+to implement it.  Calibre is released under the GPLv3.  The AGPL has additional
 restrictions for projects used as a network resource, but they don't apply to
 this project, so Phuong and Jessie can use spaCy to improve Calibre.  They'll
 have to release their code, but that was always their intention anyway.

docs/source/quickstart.rst

@@ -23,7 +23,7 @@ parser model and word vectors, which it installs within the spacy.en package dir
 
 If you're stuck using a server with an old version of Python, and you don't
 have root access, I've prepared a bootstrap script to help you compile a local
-Python install. Run:
+Python install.  Run:
 
 .. code:: bash
 
@@ -47,7 +47,7 @@ this is how I build the project.
     $ py.test tests/
 
 Python packaging is awkward at the best of times, and it's particularly tricky
-with C extensions, built via Cython, requiring large data files. So, please
+with C extensions, built via Cython, requiring large data files.  So, please
 report issues as you encounter them, and bear with me :)
 
 Usage
@@ -234,4 +234,3 @@ Features
   +---------+-----------------------------------------------------------+
   | prob    | Log probability of word, smoothed with Simple Good-Turing |
   +---------+-----------------------------------------------------------+
-

docs/source/updates.rst

@@ -7,8 +7,8 @@ Updates
 Five days ago I presented the alpha release of spaCy, a natural language
 processing library that brings state-of-the-art technology to small companies.
 
-spaCy has been very well received, and there are now a lot of eyes on the project.
-Naturally, lots of issues have surfaced.  I'm very grateful to those who've reported
+spaCy has been well received, and there are now a lot of eyes on the project.
+Naturally, lots of issues have surfaced.  I'm grateful to those who've reported
 them.  I've worked hard to address them as quickly as I could.
 
 Bug Fixes
@@ -26,13 +26,13 @@ Bug Fixes
     just store an index into that list, instead of a hash.
 
 * Parse tree navigation API was rough, and buggy.
-    The parse-tree navigation API was the last thing I added before v0.3. I've
+    The parse-tree navigation API was the last thing I added before v0.3.  I've
     now replaced it with something better.  The previous API design was flawed,
     and the implementation was buggy --- Token.child() and Token.head were
     sometimes inconsistent.
 
     I've addressed the most immediate problems, but this part of the design is
-    still a work in progress. It's a difficult problem. The parse is a tree,
+    still a work in progress.  It's a difficult problem.  The parse is a tree,
     and we want to freely navigate up and down it without creating reference
     cycles that inhibit garbage collection, and without doing a lot of copying,
     creating and deleting.
@@ -53,7 +53,7 @@ pinning down or reproducing.  Please send details of your system to the
 Enhancements: Train and evaluate on whole paragraphs
 ----------------------------------------------------
 
-.. note:: tl;dr: I shipped the wrong parsing model with 0.3. That model expected input to be segmented into sentences.  0.4 ships the correct model, which uses some algorithmic tricks to minimize the impact of tokenization and sentence segmentation errors on the parser.
+.. note:: tl;dr: I shipped the wrong parsing model with 0.3.  That model expected input to be segmented into sentences.  0.4 ships the correct model, which uses some algorithmic tricks to minimize the impact of tokenization and sentence segmentation errors on the parser.
 
 
 Most English parsing research is performed on text with perfect pre-processing:
@@ -77,7 +77,7 @@ made a big difference:
     | Corrected   | 89.9  | 88.8     |
     +-------------+-------+----------+
 
-.. note:: spaCy is evaluated on unlabelled dependencies, where the above accuracy figures refer to phrase-structure trees. Accuracies are non-comparable.
+.. note:: spaCy is evaluated on unlabelled dependencies, where the above accuracy figures refer to phrase-structure trees.  Accuracies are non-comparable.
 
 
 
@@ -108,9 +108,9 @@ input to be segmented into sentences, but with no sentence segmenter.  This
 caused a drop in parse accuracy of 4%!
 
 Over the last five days, I've worked hard to correct this.  I implemented the
-modifications to the parsing algorithm I had planned, from Dongdong Zhang et al
+modifications to the parsing algorithm I had planned, from Dongdong Zhang et al.
 (2013), and trained and evaluated the parser on raw text, using the version of
-the WSJ distributed by Read et al (2012), and used in Dridan and Oepen's
+the WSJ distributed by Read et al. (2012), and used in Dridan and Oepen's
 experiments.
 
 I'm pleased to say that on the WSJ at least, spaCy 0.4 performs almost exactly

fabfile.py

@@ -1,4 +1,4 @@
-from fabric.api import local, run, lcd, cd, env
+from fabric.api import local, lcd, env
 from os.path import exists as file_exists
 from fabtools.python import virtualenv
 from os import path

setup.py

@@ -1,16 +1,11 @@
 #!/usr/bin/env python
-import subprocess
 from setuptools import setup
-from glob import glob
 import shutil
 
 import sys
 import os
 from os import path
-from os.path import splitext
 
-
-import shutil
 from setuptools import Extension
 from distutils import sysconfig
 import platform

spacy/attrs.pxd

@@ -79,5 +79,3 @@ cpdef enum attr_id_t:
     POS
     TAG
     DEP
-
-

spacy/en/pos.pxd

@@ -22,4 +22,3 @@ cdef class EnPosTagger:
 
     cdef int set_morph(self, const int i, const PosTag* tag, TokenC* tokens) except -1
     cdef int lemmatize(self, const univ_pos_t pos, const LexemeC* lex) except -1
-

spacy/en/pos.pyx

@@ -381,4 +381,3 @@ cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
         context[7] = 4
     else:
         context[7] = 0
-

spacy/ner/context.pxd

@@ -149,5 +149,3 @@ cpdef enum:
 
 
 cdef int fill_context(atom_t* context, State* s, Tokens tokens) except -1
-
-

spacy/spans.pxd

@@ -15,4 +15,3 @@ cdef class Span:
     cdef public Span head
     cdef public list rights
     cdef public list lefts
-

spacy/syntax/ner.pyx

@@ -277,5 +277,3 @@ class OracleError(Exception):
 
 class UnknownMove(Exception):
     pass
-
-

spacy/syntax/util.py

@@ -13,5 +13,3 @@ class Config(object):
     @classmethod
     def read(cls, model_dir, name):
         return cls(**json.load(open(path.join(model_dir, '%s.json' % name))))
-
-

spacy/tokens.pyx

@@ -630,4 +630,3 @@ _parse_unset_error = """Text has not been parsed, so cannot be accessed.
 Check that the parser data is installed. Run "python -m spacy.en.download" if not.
 Check whether parse=False in the call to English.__call__
 """
-

spacy/typedefs.pxd

@@ -94,5 +94,3 @@ ctypedef uint64_t flags_t
 ctypedef uint32_t id_t
 ctypedef uint16_t len_t
 ctypedef uint16_t tag_t
-
-

spacy/util.py

@@ -1,4 +1,3 @@
-import os
 from os import path
 import codecs
 import json

spacy/vocab.pxd

@@ -33,4 +33,3 @@ cdef class Vocab:
     cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1
 
     cdef PreshMap _map
-  

tests/_depr_group_by.py

@@ -7,6 +7,7 @@ from spacy.lexeme import lex_of
 
 from spacy import LEX, NORM, SHAPE, LAST3
 
+
 def test_group_by_lex():
     tokens = en.tokenize("I like the red one and I like the blue one")
     names, hashes, groups = tokens.group_by(LEX)

tests/depr_test_ner.py

@@ -40,6 +40,7 @@ def test_begin(state, sentence):
     assert not state.is_valid('O')
     assert not state.is_valid('U-PER')
 
+
 def test_in(state, sentence):
     state.transition('B-PER')
     assert state.n_ents == 0

tests/test_align.py

@@ -30,6 +30,3 @@ def test_align_continue():
     assert aligned[2] == ('re-align', [(5, 7), (7, 8), (8, 13)])
     assert aligned[3] == ('and', [(13, 16)])
     assert aligned[4] == ('continue', [(16, 24)])
-
-
-

tests/test_array.py

@@ -37,5 +37,3 @@ def test_dep():
     assert feats_array[1][1] == tokens[1].dep
     assert feats_array[2][1] == tokens[2].dep
     assert feats_array[3][1] == tokens[3].dep
-
-

tests/test_docs.py

@@ -2,6 +2,7 @@
 """Sphinx doctest is just too hard. Manually paste doctest examples here"""
 from spacy.en.attrs import IS_LOWER
 
+
 def test_1():
     import spacy.en
     from spacy.parts_of_speech import ADV
@@ -39,6 +40,7 @@ def test2():
     nlp.vocab[u'quietly'].prob
     -11.07155704498291
 
+
 def test3():
     import spacy.en
     from spacy.parts_of_speech import ADV

tests/test_emoticons.py

@@ -8,6 +8,7 @@ from spacy.en import English
 def EN():
     return English()
 
+
 def test_tweebo_challenge(EN):
     text = u""":o :/ :'( >:o (: :) >.< XD -__- o.O ;D :-) @_@ :P 8D :1 >:( :D =| ") :> ...."""
     tokens = EN(text)

tests/test_flag_features.py

@@ -16,6 +16,7 @@ def words():
     return ["1997", "19.97", "hello9", "Hello", "HELLO", "Hello9", "\n", "!",
             "!d", "\nd"]
 
+
 def test_is_alpha(words):
     assert not is_alpha(words[0])
     assert not is_alpha(words[1])

tests/test_intern.py

@@ -5,10 +5,12 @@ from spacy.strings import StringStore
 
 import pytest
 
+
 @pytest.fixture
 def sstore():
     return StringStore()
 
+
 def test_save_bytes(sstore):
     Hello_i = sstore[b'Hello']
     assert Hello_i == 1

tests/test_iter_lexicon.py

@@ -2,10 +2,12 @@ import pytest
 
 from spacy.en import English
 
+
 @pytest.fixture
 def EN():
     return English()
 
+
 def test_range_iter(EN):
     for i in range(len(EN.vocab)):
         lex = EN.vocab[i]

tests/test_merge.py

@@ -35,4 +35,3 @@ def test_merge_heads():
 def test_issue_54():
     text = u'Talks given by women had a slightly higher number of questions asked (3.2$\pm$0.2) than talks given by men (2.6$\pm$0.1).'
     tokens = NLU(text, merge_mwes=True)
-

tests/test_morph_exceptions.py

@@ -17,6 +17,7 @@ def morph_exc():
             'PRP$': {'his': {'L': '-PRP-', 'person': 3, 'case': 2}},
            }
 
+
 def test_load_exc(EN, morph_exc):
     EN.tagger.load_morph_exceptions(morph_exc)
     tokens = EN('I like his style.', tag=True)

tests/test_ner.py

@@ -3,6 +3,7 @@ from spacy.en import English
 
 nlp = English()
 
+
 def test_simple_types():
     tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
     ents = list(tokens.ents)

tests/test_number.py

@@ -33,4 +33,3 @@ def test_word():
 def test_not_number():
     assert not like_number('dog')
     assert not like_number(',')
-

tests/test_only_punct.py

@@ -3,6 +3,7 @@ import pytest
 
 from spacy.en import English
 
+
 def test_only_pre1():
     EN = English()
     assert len(EN("(")) == 1

tests/test_parse_navigate.py

@@ -58,4 +58,3 @@ def test_child_consistency(nlp, sun_text):
         assert not children
     for head_index, children in rights.items():
         assert not children
-

tests/test_post_punct.py

@@ -49,4 +49,3 @@ def test_three_same_close(close_puncts, EN):
 def test_double_end_quote(EN):
     assert len(EN("Hello''")) == 2
     assert len(EN("''")) == 1
-

tests/test_sbd.py

@@ -3,6 +3,7 @@ from spacy.en import English
 
 import pytest
 
+
 @pytest.fixture
 def EN():
     return English()

tests/test_shape.py

@@ -8,20 +8,26 @@ from spacy.orth import word_shape as ws
 def test_capitalized():
     assert ws('Nasa') == 'Xxxx'
 
+
 def test_truncate():
     assert ws('capitalized') == 'xxxx'
 
+
 def test_digits():
     assert ws('999999999') == 'dddd'
 
+
 def test_mix():
     assert ws('C3P0') == 'XdXd'
 
+
 def test_punct():
     assert ws(',') == ','
 
+
 def test_space():
     assert ws('\n') == '\n'
 
+
 def test_punct_seq():
     assert ws('``,-') == '``,-'

tests/test_span.py

@@ -3,7 +3,6 @@ from __future__ import unicode_literals
 from spacy.en import English
 
 import pytest
-import re
 
 
 EN = English()

tests/test_special_affix.py

@@ -13,9 +13,11 @@ def EN():
 def test_no_special(EN):
     assert len(EN("(can)")) == 3
 
+
 def test_no_punct(EN):
     assert len(EN("can't")) == 2
 
+
 def test_prefix(EN):
     assert len(EN("(can't")) == 3
 

tests/test_string_loading.py

@@ -16,6 +16,3 @@ def test_one(EN):
     assert tokens[0].orth_ == 'Betty'
     tokens2 = EN('Betty also bought a pound of butter.')
     assert tokens2[0].orth_ == 'Betty'
-
-
-

tests/test_subtree.py

@@ -16,4 +16,3 @@ def test_subtrees():
     assert len(list(bus.children)) == 1
 
     assert len(list(wheels.subtree)) == 6
-

tests/test_tag_names.py

@@ -1,6 +1,7 @@
 from spacy.en import English
 import six
 
+
 def test_tag_names():
     nlp = English()
     tokens = nlp(u'I ate pizzas with anchovies.', parse=True, tag=True)

tests/test_times.py

@@ -6,6 +6,7 @@ import pytest
 
 NLU = English()
 
+
 def test_am_pm():
     numbers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12']
     variants = ['a.m.', 'am', 'p.m.', 'pm']

tests/test_token.py

@@ -4,6 +4,7 @@ import pytest
 from spacy.en import English
 from spacy.parts_of_speech import ADV
 
+
 @pytest.fixture
 def nlp():
     return English()

tests/test_token_api.py

@@ -7,6 +7,8 @@ from spacy.en.attrs import IS_STOP
 import pytest
 
 nlp = English()
+
+
 @pytest.fixture
 def token():
     tokens = nlp(u'Give it back! He pleaded.')
@@ -35,5 +37,3 @@ def test_single_token_string():
     nlp = English()
     tokens = nlp(u'foobar')
     assert tokens[0].string == 'foobar'
-
-

tests/test_token_references.py

@@ -31,6 +31,7 @@ def _orphan_from_list(toks):
         lst.append(tok)
     return lst
 
+
 def test_list_orphans():
     # Test case from NSchrading
     nlp = English()

tests/test_tokenizer.py

@@ -10,10 +10,12 @@ from spacy.en import English
 def EN():
     return English().tokenizer
 
+
 def test_no_word(EN):
     tokens = EN(u'')
     assert len(tokens) == 0
 
+
 def test_single_word(EN):
     tokens = EN(u'hello')
     assert tokens[0].orth_ == 'hello'
@@ -60,6 +62,7 @@ def test_contraction_punct(EN):
     tokens = EN("can't!")
     assert len(tokens) == 3
 
+
 def test_sample(EN):
     text = """Tributes pour in for late British Labour Party leader
 

tests/test_tokens_api.py

@@ -3,6 +3,7 @@ from spacy.en import English
 
 import pytest
 
+
 @pytest.fixture
 def tokens():
     nlp = English()

tests/test_urlish.py

@@ -2,6 +2,7 @@ from __future__ import unicode_literals
 
 from spacy.orth import like_url
 
+
 def test_basic_url():
     assert like_url('www.google.com')
     assert like_url('google.com')

tests/test_vec.py

@@ -4,15 +4,18 @@ from spacy.en import English
 
 import pytest
 
+
 @pytest.fixture
 def EN():
     return English()
 
+
 def test_vec(EN):
     hype = EN.vocab['hype']
     assert hype.orth_ == 'hype'
     assert 0.08 >= hype.repvec[0] > 0.07
 
+
 def test_capitalized(EN):
     hype = EN.vocab['Hype']
     assert hype.orth_ == 'Hype'

tests/test_whitespace.py

@@ -39,5 +39,3 @@ def test_newline_double_space(EN):
 def test_newline_space_wrap(EN):
     tokens = EN('hello \n possums')
     assert len(tokens) == 3
-
-

tests/test_wiki_sun.py

@@ -4,7 +4,6 @@ from spacy.en import English
 from spacy.util import utf8open
 
 import pytest
-import os
 from os import path