Code Coverage for nltk.corpus.reader.api
Untested Functions
|
Partially Tested Functions
|
"""
API for corpus readers.
"""
import os, re
from nltk import defaultdict
from nltk.internals import deprecated
import nltk.corpus.reader.util
from nltk.data import PathPointer, FileSystemPathPointer, ZipFilePathPointer
class CorpusReader(object):
"""
A base class for X{corpus reader} classes, each of which can be
used to read a specific corpus format. Each individual corpus
reader instance is used to read a specific corpus, consisting of
one or more files under a common root directory. Each file is
identified by its C{file identifier}, which is the relative path
to the file from the root directory.
A separate subclass is be defined for each corpus format. These
subclasses define one or more methods that provide 'views' on the
corpus contents, such as C{words()} (for a list of words) and
C{parsed_sents()} (for a list of parsed sentences). Called with
no arguments, these methods will return the contents of the entire
corpus. For most corpora, these methods define one or more
selection arguments, such as C{files} or C{categories}, which can
be used to select which portion of the corpus should be returned.
"""
def __init__(self, root, files, encoding=None, tag_mapping_function=None):
"""
@type root: L{PathPointer} or C{str}
@param root: A path pointer identifying the root directory for
this corpus. If a string is specified, then it will be
converted to a L{PathPointer} automatically.
@param files: A list of the files that make up this corpus.
This list can either be specified explicitly, as a list of
strings; or implicitly, as a regular expression over file
paths. The absolute path for each file will be constructed
by joining the reader's root to each file name.
@param encoding: The default unicode encoding for the files
that make up the corpus. C{encoding}'s value can be any
of the following:
- B{A string}: C{encoding} is the encoding name for all
files.
- B{A dictionary}: C{encoding[file_id]} is the encoding
name for the file whose identifier is C{file_id}. If
C{file_id} is not in C{encoding}, then the file
contents will be processed using non-unicode byte
strings.
- B{A list}: C{encoding} should be a list of C{(regexp,
encoding)} tuples. The encoding for a file whose
identifier is C{file_id} will be the C{encoding} value
for the first tuple whose C{regexp} matches the
C{file_id}. If no tuple's C{regexp} matches the
C{file_id}, the file contents will be processed using
non-unicode byte strings.
- C{None}: the file contents of all files will be
processed using non-unicode byte strings.
@param tag_mapping_function: A function for normalizing or
simplifying the POS tags returned by the tagged_words()
or tagged_sents() methods.
"""
if isinstance(root, basestring):
m = re.match('(.*\.zip)/?(.*)$|', root)
zipfile, zipentry = m.groups()
if zipfile:
root = ZipFilePathPointer(zipfile, zipentry)
else:
root = FileSystemPathPointer(root)
elif not isinstance(root, PathPointer):
raise TypeError('CorpusReader: expected a string or a PathPointer')
if isinstance(files, basestring):
files = nltk.corpus.reader.find_corpus_files(root, files)
self._files = tuple(files)
"""A list of the relative paths for the files that make up
this corpus."""
self._root = root
"""The root directory for this corpus."""
if isinstance(encoding, list):
encoding_dict = {}
for fileid in self._files:
for x in encoding:
(regexp, enc) = x
if re.match(regexp, fileid):
encoding_dict[fileid] = enc
break
encoding = encoding_dict
self._encoding = encoding
"""The default unicode encoding for the files that make up
this corpus. If C{encoding} is C{None}, then the file
contents are processed using byte strings (C{str})."""
self._tag_mapping_function = tag_mapping_function
def __repr__(self):
if isinstance(self._root, ZipFilePathPointer):
path = '%s/%s' % (self._root.zipfile.filename, self._root.entry)
else:
path = '%s' % self._root.path
return '<%s in %r>' % (self.__class__.__name__, path)
def files(self):
"""
Return a list of file identifiers for the files that make up
this corpus.
"""
return self._files
def abspath(self, file):
"""
Return the absolute path for the given file.
@type file: C{str}
@param file: The file identifier for the file whose path
should be returned.
@rtype: L{PathPointer}
"""
return self._root.join(file)
def abspaths(self, files=None, include_encoding=False):
"""
Return a list of the absolute paths for all files in this corpus;
or for the given list of files, if specified.
@type files: C{None} or C{str} or C{list}
@param files: Specifies the set of files for which paths should
be returned. Can be C{None}, for all files; a list of
file identifiers, for a specified set of files; or a single
file identifier, for a single file. Note that the return
value is always a list of paths, even if C{files} is a
single file identifier.
@param include_encoding: If true, then return a list of
C{(path_pointer, encoding)} tuples.
@rtype: C{list} of L{PathPointer}
"""
if files is None:
files = self._files
elif isinstance(files, basestring):
files = [files]
paths = [self._root.join(f) for f in files]
if include_encoding:
return zip(paths, [self.encoding(f) for f in files])
else:
return paths
def open(self, file):
"""
Return an open stream that can be used to read the given file.
If the file's encoding is not C{None}, then the stream will
automatically decode the file's contents into unicode.
@param file: The file identifier of the file to read.
"""
encoding = self.encoding(file)
return self._root.join(file).open(encoding)
def encoding(self, file):
"""
Return the unicode encoding for the given corpus file, if known.
If the encoding is unknown, or if the given file should be
processed using byte strings (C{str}), then return C{None}.
"""
if isinstance(self._encoding, dict):
return self._encoding.get(file)
else:
return self._encoding
def _get_root(self): return self._root
root = property(_get_root, doc="""
The directory where this corpus is stored.
@type: L{PathPointer}""")
@deprecated("Use corpus.files() instead")
def _get_items(self): return self.files()
items = property(_get_items)
@deprecated("Use corpus.abspaths() instead")
def filenames(self, items=None): return self.abspaths(items)
class CategorizedCorpusReader(object):
"""
A mixin class used to aid in the implementation of corpus readers
for categorized corpora. This class defines the method
L{categories()}, which returns a list of the categories for the
corpus or for a specified set of files; and overrides L{files()}
to take a C{categories} argument, restricting the set of files to
be returned.
Subclasses are expected to:
- Call L{__init__()} to set up the mapping.
- Override all view methods to accept a C{categories} parameter,
which can be used *instead* of the C{files} parameter, to
select which files should be included in the returned view.
"""
def __init__(self, kwargs):
"""
Initialize this mapping based on keyword arguments, as
follows:
- cat_pattern: A regular expression pattern used to find the
category for each file identifier. The pattern will be
applied to each file identifier, and the first matching
group will be used as the category label for that file.
- cat_map: A dictionary, mapping from file identifiers to
category labels.
- cat_file: The name of a file that contains the mapping
from file identifiers to categories. The argument
C{cat_delimiter} can be used to specify a delimiter.
The corresponding argument will be deleted from C{kwargs}. If
more than one argument is specified, an exception will be
raised.
"""
self._f2c = None
self._c2f = None
self._pattern = None
self._map = None
self._file = None
self._delimiter = None
if 'cat_pattern' in kwargs:
self._pattern = kwargs['cat_pattern']
del kwargs['cat_pattern']
elif 'cat_map' in kwargs:
self._map = kwargs['cat_map']
del kwargs['cat_map']
elif 'cat_file' in kwargs:
self._file = kwargs['cat_file']
del kwargs['cat_file']
if 'cat_delimiter' in kwargs:
self._delimiter = kwargs['cat_delimiter']
del kwargs['cat_delimiter']
else:
raise ValueError('Expected keyword argument cat_pattern or '
'cat_map or cat_file.')
if ('cat_pattern' in kwargs or 'cat_map' in kwargs or
'cat_file' in kwargs):
raise ValueError('Specify exactly one of: cat_pattern, '
'cat_map, cat_file.')
def _init(self):
self._f2c = defaultdict(list)
self._c2f = defaultdict(list)
if self._pattern is not None:
for file_id in self._files:
category = re.match(self._pattern, file_id).group(1)
self._add(file_id, category)
elif self._map is not None:
for (file_id, categories) in self._map.items():
for category in categories:
self._add(file_id, category)
elif self._file is not None:
for line in self.open(self._file).readlines():
line = line.strip()
file_id, categories = line.split(self._delimiter, 1)
if file_id not in self.files():
raise ValueError('In category mapping file %s: %s '
'not found' % (catfile, file_id))
for category in categories.split(self._delimiter):
self._add(file_id, category)
def _add(self, file_id, category):
self._f2c[file_id].append(category)
self._c2f[category].append(file_id)
def categories(self, files=None):
"""
Return a list of the categories that are defined for this corpus,
or for the file(s) if it is given.
"""
if self._f2c is None: self._init()
if files is None:
return sorted(self._c2f)
if isinstance(files, basestring):
files = [files]
return sorted(sum((self._f2c[d] for d in files), []))
def files(self, categories=None):
"""
Return a list of file identifiers for the files that make up
this corpus, or that make up the given category(s) if specified.
"""
if categories is None:
return super(CategorizedCorpusReader, self).files()
elif isinstance(categories, basestring):
if self._f2c is None: self._init()
return sorted(self._c2f[categories])
else:
if self._f2c is None: self._init()
return sorted(sum((self._c2f[c] for c in categories), []))