>>> import nltk.data
The nltk.data module contains functions that can be used to load NLTK resource files, such as corpora, grammars, and saved processing objects.
Resources are loaded using the function nltk.data.load(), which takes as its first argument a URL specifying what file should be loaded. The nltk: protocol loads files from the NLTK data distribution:
>>> from __future__ import print_function
>>> tokenizer = nltk.data.load('nltk:tokenizers/punkt/english.pickle')
>>> tokenizer.tokenize('Hello. This is a test. It works!')
['Hello.', 'This is a test.', 'It works!']
It is important to note that there should be no space following the colon (':') in the URL; 'nltk: tokenizers/punkt/english.pickle' will not work!
The nltk: protocol is used by default if no protocol is specified:
>>> nltk.data.load('tokenizers/punkt/english.pickle') # doctest: +ELLIPSIS
<nltk.tokenize.punkt.PunktSentenceTokenizer object at ...>
But it is also possible to load resources from http:, ftp:, and file: URLs, e.g. cfg = nltk.data.load('http://example.com/path/to/toy.cfg')
>>> # Load a grammar using an absolute path.
>>> url = 'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg')
>>> url.replace('\\', '/') # doctest: +ELLIPSIS
'file:...toy.cfg'
>>> print(nltk.data.load(url)) # doctest: +ELLIPSIS
Grammar with 14 productions (start state = S)
S -> NP VP
PP -> P NP
...
P -> 'on'
P -> 'in'
The second argument to the nltk.data.load() function specifies the file format, which determines how the file's contents are processed before they are returned by load(). The formats that are currently supported by the data module are described by the dictionary nltk.data.FORMATS:
>>> for format, descr in sorted(nltk.data.FORMATS.items()):
... print('{0:<7} {1:}'.format(format, descr)) # doctest: +NORMALIZE_WHITESPACE
cfg A context free grammar.
fcfg A feature CFG.
fol A list of first order logic expressions, parsed with
nltk.sem.logic.Expression.fromstring.
json A serialized python object, stored using the json module.
logic A list of first order logic expressions, parsed with
nltk.sem.logic.LogicParser. Requires an additional logic_parser
parameter
pcfg A probabilistic CFG.
pickle A serialized python object, stored using the pickle
module.
raw The raw (byte string) contents of a file.
text The raw (unicode string) contents of a file.
val A semantic valuation, parsed by
nltk.sem.Valuation.fromstring.
yaml A serialized python object, stored using the yaml module.
nltk.data.load() will raise a ValueError if a bad format name is specified:
>>> nltk.data.load('grammars/sample_grammars/toy.cfg', 'bar')
Traceback (most recent call last):
. . .
ValueError: Unknown format type!
By default, the "auto" format is used, which chooses a format based on the filename's extension. The mapping from file extensions to format names is specified by nltk.data.AUTO_FORMATS:
>>> for ext, format in sorted(nltk.data.AUTO_FORMATS.items()):
... print('.%-7s -> %s' % (ext, format))
.cfg -> cfg
.fcfg -> fcfg
.fol -> fol
.json -> json
.logic -> logic
.pcfg -> pcfg
.pickle -> pickle
.text -> text
.txt -> text
.val -> val
.yaml -> yaml
If nltk.data.load() is unable to determine the format based on the filename's extension, it will raise a ValueError:
>>> nltk.data.load('foo.bar')
Traceback (most recent call last):
. . .
ValueError: Could not determine format for foo.bar based on its file
extension; use the "format" argument to specify the format explicitly.
Note that by explicitly specifying the format argument, you can override the load method's default processing behavior. For example, to get the raw contents of any file, simply use format="raw":
>>> s = nltk.data.load('grammars/sample_grammars/toy.cfg', 'text')
>>> print(s) # doctest: +ELLIPSIS
S -> NP VP
PP -> P NP
NP -> Det N | NP PP
VP -> V NP | VP PP
...
The function nltk.data.retrieve() copies a given resource to a local file. This can be useful, for example, if you want to edit one of the sample grammars.
>>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg') Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy.cfg'>>> # Simulate editing the grammar. >>> with open('toy.cfg') as inp: ... s = inp.read().replace('NP', 'DP') >>> with open('toy.cfg', 'w') as out: ... _bytes_written = out.write(s)>>> # Load the edited grammar, & display it. >>> cfg = nltk.data.load('file:///' + os.path.abspath('toy.cfg')) >>> print(cfg) # doctest: +ELLIPSIS Grammar with 14 productions (start state = S) S -> DP VP PP -> P DP ... P -> 'on' P -> 'in'
The second argument to nltk.data.retrieve() specifies the filename for the new copy of the file. By default, the source file's filename is used.
>>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg', 'mytoy.cfg')
Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'mytoy.cfg'
>>> os.path.isfile('./mytoy.cfg')
True
>>> nltk.data.retrieve('grammars/sample_grammars/np.fcfg')
Retrieving 'nltk:grammars/sample_grammars/np.fcfg', saving to 'np.fcfg'
>>> os.path.isfile('./np.fcfg')
True
If a file with the specified (or default) filename already exists in the current directory, then nltk.data.retrieve() will raise a ValueError exception. It will not overwrite the file:
>>> os.path.isfile('./toy.cfg')
True
>>> nltk.data.retrieve('grammars/sample_grammars/toy.cfg') # doctest: +ELLIPSIS
Traceback (most recent call last):
. . .
ValueError: File '...toy.cfg' already exists!
The nltk.data.find() function searches the NLTK data package for a given file, and returns a pointer to that file. This pointer can either be a FileSystemPathPointer (whose path attribute gives the absolute path of the file); or a ZipFilePathPointer, specifying a zipfile and the name of an entry within that zipfile. Both pointer types define the open() method, which can be used to read the string contents of the file.
>>> path = nltk.data.find('corpora/abc/rural.txt')
>>> str(path) # doctest: +ELLIPSIS
'...rural.txt'
>>> print(path.open().read(60).decode())
PM denies knowledge of AWB kickbacks
The Prime Minister has
Alternatively, the nltk.data.load() function can be used with the keyword argument format="raw":
>>> s = nltk.data.load('corpora/abc/rural.txt', format='raw')[:60]
>>> print(s.decode())
PM denies knowledge of AWB kickbacks
The Prime Minister has
Alternatively, you can use the keyword argument format="text":
>>> s = nltk.data.load('corpora/abc/rural.txt', format='text')[:60]
>>> print(s)
PM denies knowledge of AWB kickbacks
The Prime Minister has
NLTK uses a weakref dictionary to maintain a cache of resources that have been loaded. If you load a resource that is already stored in the cache, then the cached copy will be returned. This behavior can be seen by the trace output generated when verbose=True:
>>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True)
<<Loading nltk:grammars/book_grammars/feat0.fcfg>>
>>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg', verbose=True)
<<Using cached copy of nltk:grammars/book_grammars/feat0.fcfg>>
If you wish to load a resource from its source, bypassing the cache, use the cache=False argument to nltk.data.load(). This can be useful, for example, if the resource is loaded from a local file, and you are actively editing that file:
>>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg',cache=False,verbose=True)
<<Loading nltk:grammars/book_grammars/feat0.fcfg>>
The cache no longer uses weak references. A resource will not be automatically expunged from the cache when no more objects are using it. In the following example, when we clear the variable feat0, the reference count for the feature grammar object drops to zero. However, the object remains cached:
>>> del feat0
>>> feat0 = nltk.data.load('grammars/book_grammars/feat0.fcfg',
... verbose=True)
<<Using cached copy of nltk:grammars/book_grammars/feat0.fcfg>>
You can clear the entire contents of the cache, using nltk.data.clear_cache():
>>> nltk.data.clear_cache()
>>> formulas = nltk.data.load('grammars/book_grammars/background.fol')
>>> for f in formulas: print(str(f))
all x.(boxerdog(x) -> dog(x))
all x.(boxer(x) -> person(x))
all x.-(dog(x) & person(x))
all x.(married(x) <-> exists y.marry(x,y))
all x.(bark(x) -> dog(x))
all x y.(marry(x,y) -> (person(x) & person(y)))
-(Vincent = Mia)
-(Vincent = Fido)
-(Mia = Fido)
Create a temp dir for tests that write files:
>>> import tempfile, os
>>> tempdir = tempfile.mkdtemp()
>>> old_dir = os.path.abspath('.')
>>> os.chdir(tempdir)
The retrieve() function accepts all url types:
>>> urls = ['https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg',
... 'file:%s' % nltk.data.find('grammars/sample_grammars/toy.cfg'),
... 'nltk:grammars/sample_grammars/toy.cfg',
... 'grammars/sample_grammars/toy.cfg']
>>> for i, url in enumerate(urls):
... nltk.data.retrieve(url, 'toy-%d.cfg' % i) # doctest: +ELLIPSIS
Retrieving 'https://raw.githubusercontent.com/nltk/nltk/develop/nltk/test/toy.cfg', saving to 'toy-0.cfg'
Retrieving 'file:...toy.cfg', saving to 'toy-1.cfg'
Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-2.cfg'
Retrieving 'nltk:grammars/sample_grammars/toy.cfg', saving to 'toy-3.cfg'
Clean up the temp dir:
>>> os.chdir(old_dir) >>> for f in os.listdir(tempdir): ... os.remove(os.path.join(tempdir, f)) >>> os.rmdir(tempdir)
A lazy loader is a wrapper object that defers loading a resource until it is accessed or used in any way. This is mainly intended for internal use by NLTK's corpus readers.
>>> # Create a lazy loader for toy.cfg. >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg')>>> # Show that it's not loaded yet: >>> object.__repr__(ll) # doctest: +ELLIPSIS '<nltk.data.LazyLoader object at ...>'>>> # printing it is enough to cause it to be loaded: >>> print(ll) <Grammar with 14 productions>>>> # Show that it's now been loaded: >>> object.__repr__(ll) # doctest: +ELLIPSIS '<nltk.grammar.CFG object at ...>'>>> # Test that accessing an attribute also loads it: >>> ll = nltk.data.LazyLoader('grammars/sample_grammars/toy.cfg') >>> ll.start() S >>> object.__repr__(ll) # doctest: +ELLIPSIS '<nltk.grammar.CFG object at ...>'
Write performance to gzip-compressed is extremely poor when the files become large. File creation can become a bottleneck in those cases.
Read performance from large gzipped pickle files was improved in data.py by buffering the reads. A similar fix can be applied to writes by buffering the writes to a StringIO object first.
This is mainly intended for internal use. The test simply tests that reading and writing work as intended and does not test how much improvement buffering provides.
>>> from nltk.compat import StringIO
>>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'wb', size=2**10)
>>> ans = []
>>> for i in range(10000):
... ans.append(str(i).encode('ascii'))
... test.write(str(i).encode('ascii'))
>>> test.close()
>>> test = nltk.data.BufferedGzipFile('testbuf.gz', 'rb')
>>> test.read() == b''.join(ans)
True
>>> test.close()
>>> import os
>>> os.unlink('testbuf.gz')
JSON serialization is used instead of pickle for some classes.
>>> from nltk import jsontags
>>> from nltk.jsontags import JSONTaggedEncoder, JSONTaggedDecoder, register_tag
>>> @jsontags.register_tag
... class JSONSerializable:
... json_tag = 'JSONSerializable'
...
... def __init__(self, n):
... self.n = n
...
... def encode_json_obj(self):
... return self.n
...
... @classmethod
... def decode_json_obj(cls, obj):
... n = obj
... return cls(n)
...
>>> JSONTaggedEncoder().encode(JSONSerializable(1))
'{"!JSONSerializable": 1}'
>>> JSONTaggedDecoder().decode('{"!JSONSerializable": 1}').n
1