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227 lines
8.7 KiB
ReStructuredText
227 lines
8.7 KiB
ReStructuredText
Quick Start
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===========
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Install
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-------
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.. py:currentmodule:: spacy
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With Python 2.7 or Python 3, using Linux or OSX, run:
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.. code:: bash
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$ pip install spacy
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$ python -m spacy.en.download
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.. _300 mb of data: http://s3-us-west-1.amazonaws.com/media.spacynlp.com/en_data_all-0.4.tgz
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The download command fetches and installs about 300mb of data, for the
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parser model and word vectors, which it installs within the spacy.en package directory.
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If you're stuck using a server with an old version of Python, and you don't
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have root access, I've prepared a bootstrap script to help you compile a local
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Python install. Run:
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.. code:: bash
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$ curl https://raw.githubusercontent.com/honnibal/spaCy/master/bootstrap_python_env.sh | bash && source .env/bin/activate
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The other way to install the package is to clone the github repository, and
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build it from source. This installs an additional dependency, Cython.
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If you're using Python 2, I also recommend installing fabric and fabtools ---
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this is how I build the project.
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.. code:: bash
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$ git clone https://github.com/honnibal/spaCy.git
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$ cd spaCy
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$ virtualenv .env && source .env/bin/activate
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$ export PYTHONPATH=`pwd`
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$ pip install -r requirements.txt
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$ python setup.py build_ext --inplace
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$ python -m spacy.en.download
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$ pip install pytest
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$ py.test tests/
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Python packaging is awkward at the best of times, and it's particularly tricky
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with C extensions, built via Cython, requiring large data files. So, please
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report issues as you encounter them, and bear with me :)
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Usage
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-----
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The main entry-point is :meth:`en.English.__call__`, which accepts a unicode string
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as an argument, and returns a :py:class:`tokens.Tokens` object. You can
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iterate over it to get :py:class:`tokens.Token` objects, which provide
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a convenient API:
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>>> from __future__ import unicode_literals # If Python 2
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>>> from spacy.en import English
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>>> nlp = English()
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>>> tokens = nlp(u'I ate the pizza with anchovies.')
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>>> pizza = tokens[3]
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>>> (pizza.orth, pizza.orth_, pizza.head.lemma, pizza.head.lemma_)
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... (14702, u'pizza', 14702, u'eat')
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spaCy maps all strings to sequential integer IDs --- a common trick in NLP.
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If an attribute `Token.foo` is an integer ID, then `Token.foo_` is the string,
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e.g. `pizza.orth_` and `pizza.orth` provide the integer ID and the string of
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the original orthographic form of the word.
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.. note:: en.English.__call__ is stateful --- it has an important **side-effect**.
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When it processes a previously unseen word, it increments the ID counter,
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assigns the ID to the string, and writes the mapping in
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:py:data:`English.vocab.strings` (instance of
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:py:class:`strings.StringStore`).
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Future releases will feature a way to reconcile mappings, but for now, you
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should only work with one instance of the pipeline at a time.
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(Most of the) API at a glance
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-----------------------------
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**Process the string:**
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.. py:class:: spacy.en.English(self, data_dir=join(dirname(__file__), 'data'))
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.. py:method:: __call__(self, text: unicode, tag=True, parse=False) --> Tokens
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+-----------------+--------------+--------------+
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| Attribute | Type | Its API |
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+=================+==============+==============+
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| vocab | Vocab | __getitem__ |
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+-----------------+--------------+--------------+
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| vocab.strings | StingStore | __getitem__ |
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+-----------------+--------------+--------------+
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| tokenizer | Tokenizer | __call__ |
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+-----------------+--------------+--------------+
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| tagger | EnPosTagger | __call__ |
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+-----------------+--------------+--------------+
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| parser | GreedyParser | __call__ |
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+-----------------+--------------+--------------+
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**Get dict or numpy array:**
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.. py:method:: tokens.Tokens.to_array(self, attr_ids: List[int]) --> ndarray[ndim=2, dtype=long]
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.. py:method:: tokens.Tokens.count_by(self, attr_id: int) --> Dict[int, int]
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**Get Token objects**
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.. py:method:: tokens.Tokens.__getitem__(self, i) --> Token
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.. py:method:: tokens.Tokens.__iter__(self) --> Iterator[Token]
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**Embedded word representenations**
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.. py:attribute:: tokens.Token.repvec
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.. py:attribute:: lexeme.Lexeme.repvec
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**Navigate to tree- or string-neighbor tokens**
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.. py:method:: nbor(self, i=1) --> Token
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.. py:method:: child(self, i=1) --> Token
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.. py:method:: sibling(self, i=1) --> Token
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.. py:attribute:: head: Token
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.. py:attribute:: dep: int
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**Align to original string**
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.. py:attribute:: string: unicode
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Padded with original whitespace.
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.. py:attribute:: length: int
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Length, in unicode code-points. Equal to len(self.orth_).
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.. py:attribute:: idx: int
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Starting offset of word in the original string.
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Features
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--------
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**Boolean features**
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>>> lexeme = nlp.vocab[u'Apple']
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>>> lexeme.is_alpha, is_upper
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True, False
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>>> tokens = nlp('Apple computers')
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>>> tokens[0].is_alpha, tokens[0].is_upper
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>>> True, False
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>>> from spact.en.attrs import IS_ALPHA, IS_UPPER
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>>> tokens.to_array((IS_ALPHA, IS_UPPER))[0]
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array([1, 0])
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+----------+---------------------------------------------------------------+
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| is_alpha | :py:meth:`str.isalpha` |
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+----------+---------------------------------------------------------------+
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| is_digit | :py:meth:`str.isdigit` |
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+----------+---------------------------------------------------------------+
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| is_lower | :py:meth:`str.islower` |
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+----------+---------------------------------------------------------------+
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| is_title | :py:meth:`str.istitle` |
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+----------+---------------------------------------------------------------+
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| is_upper | :py:meth:`str.isupper` |
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+----------+---------------------------------------------------------------+
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| is_ascii | all(ord(c) < 128 for c in string) |
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+----------+---------------------------------------------------------------+
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| is_punct | all(unicodedata.category(c).startswith('P') for c in string) |
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+----------+---------------------------------------------------------------+
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| like_url | Using various heuristics, does the string resemble a URL? |
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+----------+---------------------------------------------------------------+
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| like_num | "Two", "10", "1,000", "10.54", "1/2" etc all match |
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+----------+---------------------------------------------------------------+
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**String-transform Features**
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+----------+---------------------------------------------------------------+
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| orth | The original string, unmodified. |
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+----------+---------------------------------------------------------------+
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| lower | The original string, forced to lower-case |
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+----------+---------------------------------------------------------------+
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| norm | The string after additional normalization |
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+----------+---------------------------------------------------------------+
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| shape | Word shape, e.g. 10 --> dd, Garden --> Xxxx, Hi!5 --> Xx!d |
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+----------+---------------------------------------------------------------+
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| prefix | A short slice from the start of the string. |
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+----------+---------------------------------------------------------------+
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| suffix | A short slice from the end of the string. |
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+----------+---------------------------------------------------------------+
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| lemma | The word's lemma, i.e. morphological suffixes removed |
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+----------+---------------------------------------------------------------+
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**Syntactic labels**
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+----------+---------------------------------------------------------------+
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| pos | The word's part-of-speech, from the Google Universal Tag Set |
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+----------+---------------------------------------------------------------+
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| tag | A fine-grained morphosyntactic tag, e.g. VBZ, NNS, etc |
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+----------+---------------------------------------------------------------+
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| dep | Dependency type label between word and its head, e.g. subj |
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+----------+---------------------------------------------------------------+
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**Distributional**
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+---------+-----------------------------------------------------------+
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| cluster | Brown cluster ID of the word |
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+---------+-----------------------------------------------------------+
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| prob | Log probability of word, smoothed with Simple Good-Turing |
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+---------+-----------------------------------------------------------+
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