def fetch_bernie_and_hillary(data_dir=None,
download_if_missing=True,
shuffle=False):
"""
Load the Bernie & Hillary corpus from disk (automatically downloading data
from S3 if necessary and desired).
Args:
data_dir (str): path on disk from which corpus will be loaded;
if None, textacy's default data_dir is used (optional)
download_if_missing (bool): if True and corpus not found on disk, it will
be automatically downloaded from S3 and saved to disk (optional)
shuffle (bool): if True, randomly shuffle order of documents;
if False, documents are sorted chronologically (optional)
Returns:
list[dict]: each item in list corresponds to a speech document
Raises:
IOError: if file is not found on disk and `download_if_missing` is False
HTTPError: if file is not found on disk, `download_if_missing` is True,
and something goes wrong with the download
.. warn:: The Bernie & Hillary corpus has been deprecated! Use the newer and
more comprehensive CapitolWords corpus instead. To recreate B&H, filter
CapitolWords speeches by `speaker_name={'Bernie Sanders', 'Hillary Clinton'}`.
"""
with warnings.catch_warnings():
warnings.simplefilter('always', DeprecationWarning)
msg = """
The Bernie & Hillary corpus has been deprecated! Use the newer and
more comprehensive CapitolWords corpus instead. To recreate B&H,
filter CapitolWords speeches by `speaker_name={'Bernie Sanders', 'Hillary Clinton'}`.
"""
warnings.warn(msg.strip(),
DeprecationWarning)
if data_dir is None:
data_dir = DEFAULT_DATA_DIR
fname = os.path.join(data_dir, FNAME)
try:
data = list(read_json_lines(fname, mode='rt', encoding=None))
except (OSError, IOError):
if download_if_missing is True:
_download_bernie_and_hillary(data_dir=data_dir)
data = list(read_json_lines(fname, mode='rt', encoding=None))
else:
logger.exception('unable to load corpus from %s', fname)
raise
logger.info('loading corpus from %s', fname)
if shuffle is True:
random.shuffle(data)
return data
def fetch_bernie_and_hillary(data_dir=None,
download_if_missing=True,
shuffle=False):
"""
Load the Bernie & Hillary corpus from disk (automatically downloading data
from S3 if necessary and desired).
Args:
data_dir (str): path on disk from which corpus will be loaded;
if None, textacy's default data_dir is used (optional)
download_if_missing (bool): if True and corpus not found on disk, it will
be automatically downloaded from S3 and saved to disk (optional)
shuffle (bool): if True, randomly shuffle order of documents;
if False, documents are sorted chronologically (optional)
Returns:
list[dict]: each item in list corresponds to a speech document
Raises:
IOError: if file is not found on disk and `download_if_missing` is False
HTTPError: if file is not found on disk, `download_if_missing` is True,
and something goes wrong with the download
.. warn:: The Bernie & Hillary corpus has been deprecated! Use the newer and
more comprehensive CapitolWords corpus instead. To recreate B&H, filter
CapitolWords speeches by `speaker_name={'Bernie Sanders', 'Hillary Clinton'}`.
"""
with warnings.catch_warnings():
warnings.simplefilter('always', DeprecationWarning)
msg = """
The Bernie & Hillary corpus has been deprecated! Use the newer and
more comprehensive CapitolWords corpus instead. To recreate B&H,
filter CapitolWords speeches by `speaker_name={'Bernie Sanders', 'Hillary Clinton'}`.
"""
warnings.warn(msg.strip(), DeprecationWarning)
if data_dir is None:
data_dir = DEFAULT_DATA_DIR
fname = os.path.join(data_dir, FNAME)
try:
data = list(read_json_lines(fname, mode='rt', encoding=None))
except (OSError, IOError):
if download_if_missing is True:
_download_bernie_and_hillary(data_dir=data_dir)
data = list(read_json_lines(fname, mode='rt', encoding=None))
else:
logger.exception('unable to load corpus from %s', fname)
raise
logger.info('loading corpus from %s', fname)
if shuffle is True:
random.shuffle(data)
return data
def _iterate(self, text_only, subreddit, date_range, score_range, min_len,
limit):
"""
Low-level method to iterate over the records in this dataset. Used by
:meth:`RedditComments.texts()` and :meth:`RedditComments.records()`.
"""
if subreddit:
if isinstance(subreddit, compat.string_types):
subreddit = {subreddit}
elif isinstance(subreddit, (list, tuple)):
subreddit = set(subreddit)
if score_range:
score_range = self._parse_score_range(score_range)
if date_range:
date_range = self._parse_date_range(date_range)
needed_filenames = {
os.path.join(self.data_dir, fstub)
for fstub in self._generate_filestubs(date_range)
}
filenames = tuple(fname for fname in self.filenames
if fname in needed_filenames)
else:
filenames = self.filenames
if not filenames:
raise IOError(
'No files found at {} corresponding to date range {}'.format(
self.data_dir, date_range))
n = 0
for filename in filenames:
for line in fileio.read_json_lines(filename, mode='rb'):
if subreddit and line['subreddit'] not in subreddit:
continue
if score_range and not score_range[0] <= line[
'score'] < score_range[1]:
continue
line['created_utc'] = self._convert_timestamp(
line.get('created_utc', ''))
if date_range and not date_range[0] <= line[
'created_utc'] < date_range[1]:
continue
line['body'] = self._clean_content(line['body'])
if min_len and len(line['body']) < min_len:
continue
if text_only is True:
yield line['body']
else:
line['retrieved_on'] = self._convert_timestamp(
line.get('retrieved_on', ''))
yield line
n += 1
if n == limit:
break
if n == limit:
break
def test_read_write_json_lines(self):
expected = [{'idx': i, 'sent': sent.text}
for i, sent in enumerate(self.spacy_doc.sents)]
filename = os.path.join(self.tempdir, 'test_read_write_json_lines.json')
fileio.write_json_lines(expected, filename)
observed = list(fileio.read_json_lines(filename))
self.assertEqual(observed, expected)
def _iterate(self, text_only, subreddit, date_range, score_range, min_len, limit):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
if subreddit:
if isinstance(subreddit, string_types):
subreddit = {subreddit}
elif isinstance(subreddit, (list, tuple)):
subreddit = set(subreddit)
if date_range:
if not isinstance(date_range, (list, tuple)):
msg = "`date_range` must be a list or tuple, not {}".format(type(date_range))
raise ValueError(msg)
if not len(date_range) == 2:
msg = "`date_range` must have both start and end values"
raise ValueError(msg)
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
if score_range:
if not isinstance(score_range, (list, tuple)):
msg = "`score_range` must be a list or tuple, not {}".format(type(score_range))
raise ValueError(msg)
if len(score_range) != 2:
msg = "`score_range` must have both min and max values"
raise ValueError(msg)
if not score_range[0]:
score_range = (MIN_INT, score_range[1])
if not score_range[1]:
score_range = (score_range[0], MAX_INT)
n = 0
mode = "rb" if PY2 else "rt" # Python 2 can't open json in text mode
for path in self.paths:
for line in read_json_lines(path, mode=mode):
if subreddit and line["subreddit"] not in subreddit:
continue
if score_range and not score_range[0] <= line["score"] <= score_range[1]:
continue
line["created_utc"] = self._convert_timestamp(line.get("created_utc", ""))
if date_range and not date_range[0] <= line["created_utc"] <= date_range[1]:
continue
line["body"] = self._clean_content(line["body"])
if min_len and len(line["body"]) < min_len:
continue
if text_only is True:
yield line["body"]
else:
line["retrieved_on"] = self._convert_timestamp(line.get("retrieved_on", ""))
yield line
n += 1
if n == limit:
break
if n == limit:
break
def load(cls, path, fname_prefix=None, compression=None):
"""
Load serialized content and metadata from disk, and initialize a TextCorpus.
Args:
path (str): directory on disk where content + metadata are saved
fname_prefix (str, optional): additional identifying information
prepended to standard filenames 'spacy_docs.bin' and 'metadatas.json'
when saving to disk
compression ({'gzip', 'bz2', 'lzma'} or None): type of compression
used to reduce size of metadatas json file
Returns:
:class:`textacy.TextCorpus`
.. warn:: If the `spacy.Vocab` object used to save this corpus is not the
same as the one used to load it, there will be problems! Consequently,
this functionality is only useful as short-term but not long-term storage.
"""
if fname_prefix:
info_fname = os.path.join(path,
'_'.join([fname_prefix, 'info.json']))
meta_fname = os.path.join(
path, '_'.join([fname_prefix, 'metadatas.json']))
docs_fname = os.path.join(
path, '_'.join([fname_prefix, 'spacy_docs.bin']))
else:
info_fname = os.path.join(path, 'info.json')
meta_fname = os.path.join(path, 'metadatas.json')
docs_fname = os.path.join(path, 'spacy_docs.bin')
meta_fname = meta_fname + ('.gz' if compression == 'gzip' else
'.bz2' if compression == 'bz2' else
'.xz' if compression == 'lzma' else '')
meta_mode = 'rt' if PY2 is False or compression is None else 'rb'
package_info = list(fileio.read_json(info_fname))[0]
lang = package_info['textacy_lang']
spacy_version = package_info['spacy_version']
if spacy_version != spacy.about.__version__:
msg = """
the spaCy version used to save this TextCorpus to disk is not the
same as the version currently installed ('{}' vs. '{}'); if the
data underlying the associated `spacy.Vocab` has changed, this
loaded TextCorpus may not be valid!
""".format(spacy_version, spacy.about.__version__)
warnings.warn(msg, UserWarning)
textcorpus = TextCorpus(lang)
metadata_stream = fileio.read_json_lines(
meta_fname,
mode=meta_mode,
)
spacy_docs = fileio.read_spacy_docs(textcorpus.spacy_vocab, docs_fname)
for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
textcorpus.add_doc(
TextDoc(spacy_doc,
spacy_pipeline=textcorpus.spacy_pipeline,
lang=lang,
metadata=metadata))
return textcorpus
def _iterate(self, text_only, opinion_author=None, decision_direction=None,
issue_area=None, date_range=None, min_len=None, limit=-1):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
# prepare filters
if opinion_author:
if isinstance(opinion_author, int):
opinion_author = {opinion_author}
if not all(oa in self.opinion_author_codes for oa in opinion_author):
msg = 'invalid `opinion_author` value; see `SupremeCourt.opinion_author_codes`'
raise ValueError(msg)
if issue_area:
if isinstance(issue_area, int):
issue_area = {issue_area}
if not all(ii in self.issue_area_codes for ii in issue_area):
msg = 'invalid `issue_area` value; see `SupremeCourt.issue_area_codes`'
raise ValueError(msg)
if decision_direction:
if isinstance(decision_direction, string_types):
decision_direction = {decision_direction}
if not all(dd in self.decision_directions for dd in decision_direction):
msg = 'invalid `decision_direction` value; see `SupremeCourt.decision_directions`'
raise ValueError(msg)
if date_range:
if not isinstance(date_range, (list, tuple)):
msg = '`date_range` must be a list or tuple, not {}'.format(type(date_range))
raise ValueError(msg)
if not len(date_range) == 2:
msg = '`date_range` must have both start and end values'
raise ValueError(msg)
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
n = 0
mode = 'rb' if PY2 else 'rt'
for line in read_json_lines(self.filepath, mode=mode):
if opinion_author and line['maj_opinion_author'] not in opinion_author:
continue
if issue_area and line['issue_area'] not in issue_area:
continue
if decision_direction and line['decision_direction'] not in decision_direction:
continue
if date_range and not date_range[0] <= line['decision_date'] <= date_range[1]:
continue
if min_len and len(line['text']) < min_len:
continue
if text_only is True:
yield line['text']
else:
yield line
n += 1
if n == limit:
break
def _iterate(self, text_only, opinion_author=None, decision_direction=None,
issue_area=None, date_range=None, min_len=None, limit=-1):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
# prepare filters
if opinion_author:
if isinstance(opinion_author, int):
opinion_author = {opinion_author}
if not all(oa in self.opinion_author_codes for oa in opinion_author):
msg = 'invalid `opinion_author` value; see `SupremeCourt.opinion_author_codes`'
raise ValueError(msg)
if issue_area:
if isinstance(issue_area, int):
issue_area = {issue_area}
if not all(ii in self.issue_area_codes for ii in issue_area):
msg = 'invalid `issue_area` value; see `SupremeCourt.issue_area_codes`'
raise ValueError(msg)
if decision_direction:
if isinstance(decision_direction, string_types):
decision_direction = {decision_direction}
if not all(dd in self.decision_directions for dd in decision_direction):
msg = 'invalid `decision_direction` value; see `SupremeCourt.decision_directions`'
raise ValueError(msg)
if date_range:
if not isinstance(date_range, (list, tuple)):
msg = '`date_range` must be a list or tuple, not {}'.format(type(date_range))
raise ValueError(msg)
if not len(date_range) == 2:
msg = '`date_range` must have both start and end values'
raise ValueError(msg)
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
n = 0
mode = 'rb' if is_python2 else 'rt'
for line in read_json_lines(self.filepath, mode=mode):
if opinion_author and line['maj_opinion_author'] not in opinion_author:
continue
if issue_area and line['issue_area'] not in issue_area:
continue
if decision_direction and line['decision_direction'] not in decision_direction:
continue
if date_range and not date_range[0] <= line['decision_date'] <= date_range[1]:
continue
if min_len and len(line['text']) < min_len:
continue
if text_only is True:
yield line['text']
else:
yield line
n += 1
if n == limit:
break
def test_read_write_json_lines(self):
expected = [{
'idx': i,
'sent': sent.text
} for i, sent in enumerate(self.spacy_doc.sents)]
filename = os.path.join(self.tempdir,
'test_read_write_json_lines.json')
fileio.write_json_lines(expected, filename)
observed = list(fileio.read_json_lines(filename))
self.assertEqual(observed, expected)
def fetch_bernie_and_hillary(data_dir=None,
download_if_missing=True,
shuffle=False):
"""
Load the Bernie & Hillary corpus from disk (automatically downloading data
from S3 if necessary and desired).
Args:
data_dir (str): path on disk from which corpus will be loaded;
if None, textacy's default data_dir is used (optional)
download_if_missing (bool): if True and corpus not found on disk, it will
be automatically downloaded from S3 and saved to disk (optional)
shuffle (bool): if True, randomly shuffle order of documents;
if False, documents are sorted chronologically (optional)
Returns:
list[dict]: each item in list corresponds to a speech document
Raises:
IOError: if file is not found on disk and `download_if_missing` is False
HTTPError: if file is not found on disk, `download_if_missing` is True,
and something goes wrong with the download
"""
if data_dir is None:
data_dir = DEFAULT_DATA_DIR
fname = os.path.join(data_dir, FNAME)
try:
data = list(read_json_lines(fname, mode='rt', encoding=None))
except (OSError, IOError):
if download_if_missing is True:
_download_bernie_and_hillary(data_dir=data_dir)
data = list(read_json_lines(fname, mode='rt', encoding=None))
else:
logger.exception('unable to load corpus from %s', fname)
raise
logger.info('loading corpus from %s', fname)
if shuffle is True:
random.shuffle(data)
return data
def fetch_bernie_and_hillary(data_dir=None,
download_if_missing=True,
shuffle=False):
"""
Load the Bernie & Hillary corpus from disk (automatically downloading data
from S3 if necessary and desired).
Args:
data_dir (str): path on disk from which corpus will be loaded;
if None, textacy's default data_dir is used (optional)
download_if_missing (bool): if True and corpus not found on disk, it will
be automatically downloaded from S3 and saved to disk (optional)
shuffle (bool): if True, randomly shuffle order of documents;
if False, documents are sorted chronologically (optional)
Returns:
list[dict]: each item in list corresponds to a speech document
Raises:
IOError: if file is not found on disk and `download_if_missing` is False
HTTPError: if file is not found on disk, `download_if_missing` is True,
and something goes wrong with the download
"""
if data_dir is None:
data_dir = DEFAULT_DATA_DIR
fname = os.path.join(data_dir, FNAME)
try:
data = list(read_json_lines(fname, mode='rt', encoding=None))
except (OSError, IOError):
if download_if_missing is True:
_download_bernie_and_hillary(data_dir=data_dir)
data = list(read_json_lines(fname, mode='rt', encoding=None))
else:
logger.exception('unable to load corpus from %s', fname)
raise
logger.info('loading corpus from %s', fname)
if shuffle is True:
random.shuffle(data)
return data
def load(cls, path, name=None, compression=None):
"""
Load content and metadata from disk, and initialize a ``Corpus``.
Args:
path (str): Directory on disk where content + metadata are saved.
name (str): Identifying/uniquifying name prepended to the default
filenames 'spacy_docs.bin', 'metadatas.json', and 'info.json',
used when corpus was saved to disk via :meth:`Corpus.save()`.
compression ({'gzip', 'bz2', 'lzma'} or None): Type of compression
used to reduce size of 'metadatas.json' file when saved, if any.
Returns:
:class:`textacy.Corpus <Corpus>`
.. warning:: If the ``spacy.Vocab`` object used to save this document is
not the same as the one used to load it, there will be problems!
Consequently, this functionality is only useful as short-term but
not long-term storage.
"""
if name:
info_fname = os.path.join(path, '_'.join([name, 'info.json']))
meta_fname = os.path.join(path, '_'.join([name, 'metadatas.json']))
docs_fname = os.path.join(path, '_'.join([name, 'spacy_docs.bin']))
else:
info_fname = os.path.join(path, 'info.json')
meta_fname = os.path.join(path, 'metadatas.json')
docs_fname = os.path.join(path, 'spacy_docs.bin')
meta_fname = meta_fname + ('.gz' if compression == 'gzip'
else '.bz2' if compression == 'bz2'
else '.xz' if compression == 'lzma'
else '')
meta_mode = 'rt' if PY2 is False or compression is None else 'rb'
package_info = list(fileio.read_json(info_fname))[0]
lang = package_info['textacy_lang']
spacy_version = package_info['spacy_version']
if spacy_version != spacy.about.__version__:
msg = """
the spaCy version used to save this Corpus to disk is not the
same as the version currently installed ('{}' vs. '{}'); if the
data underlying the associated `spacy.Vocab` has changed, this
loaded Corpus may not be valid!
""".format(spacy_version, spacy.about.__version__)
warnings.warn(msg, UserWarning)
corpus = Corpus(lang)
metadata_stream = fileio.read_json_lines(meta_fname, mode=meta_mode)
spacy_docs = fileio.read_spacy_docs(corpus.spacy_vocab, docs_fname)
for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
corpus.add_doc(
Doc(spacy_doc, lang=corpus.spacy_lang, metadata=metadata))
return corpus
def load(cls, path, fname_prefix=None, compression=None):
"""
Load serialized content and metadata from disk, and initialize a TextCorpus.
Args:
path (str): directory on disk where content + metadata are saved
fname_prefix (str, optional): additional identifying information
prepended to standard filenames 'spacy_docs.bin' and 'metadatas.json'
when saving to disk
compression ({'gzip', 'bz2', 'lzma'} or None): type of compression
used to reduce size of metadatas json file
Returns:
:class:`textacy.TextCorpus`
.. warn:: If the `spacy.Vocab` object used to save this corpus is not the
same as the one used to load it, there will be problems! Consequently,
this functionality is only useful as short-term but not long-term storage.
"""
if fname_prefix:
info_fname = os.path.join(path, '_'.join([fname_prefix, 'info.json']))
meta_fname = os.path.join(path, '_'.join([fname_prefix, 'metadatas.json']))
docs_fname = os.path.join(path, '_'.join([fname_prefix, 'spacy_docs.bin']))
else:
info_fname = os.path.join(path, 'info.json')
meta_fname = os.path.join(path, 'metadatas.json')
docs_fname = os.path.join(path, 'spacy_docs.bin')
meta_fname = meta_fname + ('.gz' if compression == 'gzip'
else '.bz2' if compression == 'bz2'
else '.xz' if compression == 'lzma'
else '')
meta_mode = 'rt' if PY2 is False or compression is None else 'rb'
package_info = list(fileio.read_json(info_fname))[0]
lang = package_info['textacy_lang']
spacy_version = package_info['spacy_version']
if spacy_version != spacy.about.__version__:
msg = """
the spaCy version used to save this TextCorpus to disk is not the
same as the version currently installed ('{}' vs. '{}'); if the
data underlying the associated `spacy.Vocab` has changed, this
loaded TextCorpus may not be valid!
""".format(spacy_version, spacy.about.__version__)
warnings.warn(msg, UserWarning)
textcorpus = TextCorpus(lang)
metadata_stream = fileio.read_json_lines(meta_fname, mode=meta_mode,)
spacy_docs = fileio.read_spacy_docs(textcorpus.spacy_vocab, docs_fname)
for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
textcorpus.add_doc(
TextDoc(spacy_doc, spacy_pipeline=textcorpus.spacy_pipeline,
lang=lang, metadata=metadata))
return textcorpus
def load(cls, path, name=None, compression=None):
"""
Load content and metadata from disk, and initialize a ``Corpus``.
Args:
path (str): Directory on disk where content + metadata are saved.
name (str): Identifying/uniquifying name prepended to the default
filenames 'spacy_docs.bin', 'metadatas.json', and 'info.json',
used when corpus was saved to disk via :meth:`Corpus.save()`.
compression ({'gzip', 'bz2', 'lzma'} or None): Type of compression
used to reduce size of 'metadatas.json' file when saved, if any.
Returns:
:class:`textacy.Corpus <Corpus>`
.. warning:: If the ``spacy.Vocab`` object used to save this document is
not the same as the one used to load it, there will be problems!
Consequently, this functionality is only useful as short-term but
not long-term storage.
"""
if name:
info_fname = os.path.join(path, '_'.join([name, 'info.json']))
meta_fname = os.path.join(path, '_'.join([name, 'metadatas.json']))
docs_fname = os.path.join(path, '_'.join([name, 'spacy_docs.bin']))
else:
info_fname = os.path.join(path, 'info.json')
meta_fname = os.path.join(path, 'metadatas.json')
docs_fname = os.path.join(path, 'spacy_docs.bin')
meta_fname = meta_fname + ('.gz' if compression == 'gzip'
else '.bz2' if compression == 'bz2'
else '.xz' if compression == 'lzma'
else '')
meta_mode = 'rt' if PY2 is False or compression is None else 'rb'
package_info = list(fileio.read_json(info_fname))[0]
lang = package_info['textacy_lang']
spacy_version = package_info['spacy_version']
if spacy_version != spacy.about.__version__:
msg = """
the spaCy version used to save this Corpus to disk is not the
same as the version currently installed ('{}' vs. '{}'); if the
data underlying the associated `spacy.Vocab` has changed, this
loaded Corpus may not be valid!
""".format(spacy_version, spacy.about.__version__)
warnings.warn(msg, UserWarning)
corpus = Corpus(lang)
metadata_stream = fileio.read_json_lines(meta_fname, mode=meta_mode)
spacy_docs = fileio.read_spacy_docs(corpus.spacy_vocab, docs_fname)
for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
corpus.add_doc(
Doc(spacy_doc, lang=corpus.spacy_lang, metadata=metadata))
return corpus
def test_read_write_json_lines_unicode(self):
expected = [{'idx': i, 'sent': sent.text}
for i, sent in enumerate(self.spacy_doc.sents)]
for ext in ('.json', '.json.gz', '.json.bz2', '.json.xz'):
filename = os.path.join(
self.tempdir, 'test_read_write_json_lines_unicode' + ext)
if PY2 is True and ext != '.json':
self.assertRaises(
ValueError, fileio.open_sesame,
filename, 'wt', None, True)
else:
fileio.write_json_lines(expected, filename, mode='wt',
auto_make_dirs=True)
observed = list(fileio.read_json_lines(filename, mode='rt'))
self.assertEqual(observed, expected)
def test_read_write_json_lines_unicode(self):
expected = [{'idx': i, 'sent': sent.text}
for i, sent in enumerate(self.spacy_doc.sents)]
for ext in ('.json', '.json.gz', '.json.bz2', '.json.xz'):
filename = os.path.join(
self.tempdir, 'test_read_write_json_lines_unicode' + ext)
if PY2 is True and ext != '.json':
self.assertRaises(
ValueError, fileio.open_sesame,
filename, 'wt', None, True)
else:
fileio.write_json_lines(expected, filename, mode='wt',
auto_make_dirs=True)
observed = list(fileio.read_json_lines(filename, mode='rt'))
self.assertEqual(observed, expected)
def load(cls, path, fname_prefix=None):
"""
Load serialized content and metadata from disk, and initialize a TextCorpus.
Args:
path (str): directory on disk where content + metadata are saved
fname_prefix (str, optional): additional identifying information
prepended to standard filenames 'spacy_docs.bin' and 'metadatas.json'
when saving to disk
Returns:
:class:`textacy.TextCorpus`
"""
if fname_prefix:
info_fname = os.path.join(path, '_'.join([fname_prefix, 'info.json']))
meta_fname = os.path.join(path, '_'.join([fname_prefix, 'metadatas.json']))
docs_fname = os.path.join(path, '_'.join([fname_prefix, 'spacy_docs.bin']))
else:
info_fname = os.path.join(path, 'info.json')
meta_fname = os.path.join(path, 'metadatas.json')
docs_fname = os.path.join(path, 'spacy_docs.bin')
package_info = list(fileio.read_json(info_fname))[0]
lang = package_info['textacy_lang']
spacy_version = package_info['spacy_version']
if spacy_version != spacy.about.__version__:
msg = """
the spaCy version used to save this TextCorpus to disk is not the
same as the version currently installed ('{}' vs. '{}'); if the
data underlying the associated `spacy.Vocab` has changed, this
loaded TextCorpus may not be valid!
""".format(spacy_version, spacy.about.__version__)
warnings.warn(msg, UserWarning)
textcorpus = TextCorpus(lang)
metadata_stream = fileio.read_json_lines(meta_fname)
spacy_docs = fileio.read_spacy_docs(textcorpus.spacy_vocab, docs_fname)
for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
textcorpus.add_doc(
TextDoc(spacy_doc, spacy_pipeline=textcorpus.spacy_pipeline,
lang=lang, metadata=metadata))
return textcorpus
def test_read_write_json_lines_bytes(self):
expected = [{
'idx': i,
'sent': sent.text
} for i, sent in enumerate(self.spacy_doc.sents)]
for ext in ('.json', '.json.gz', '.json.bz2', '.json.xz'):
filename = os.path.join(self.tempdir,
'test_read_write_json_lines_bytes' + ext)
if is_python2 is True:
if ext == '.json.xz':
self.assertRaises(ValueError, fileio.open_sesame, filename,
'wb', 'utf-8', True)
else:
fileio.write_json_lines(expected,
filename,
mode='wb',
auto_make_dirs=True)
observed = list(fileio.read_json_lines(filename,
mode='rb'))
self.assertEqual(observed, expected)
else:
self.assertRaises(TypeError, fileio.write_json_lines, expected,
filename, 'wb', None, True)
def _iterate(self,
text_only,
opinion_author=None,
decision_direction=None,
issue_area=None,
date_range=None,
min_len=None,
limit=-1):
"""
Low-level method to iterate over the records in this dataset. Used by
:meth:`SupremeCourt.texts()` and :meth:`SupremeCourt.records()`.
"""
if not self.filename:
raise IOError('{} file not found'.format(self._filename))
if opinion_author:
if isinstance(opinion_author, int):
opinion_author = {opinion_author}
if not all(oa in self.opinion_author_codes
for oa in opinion_author):
msg = 'invalid `opinion_author` value; see `SupremeCourt.opinion_author_codes`'
raise ValueError(msg)
if issue_area:
if isinstance(issue_area, int):
issue_area = {issue_area}
if not all(ii in self.issue_area_codes for ii in issue_area):
msg = 'invalid `issue_area` value; see `SupremeCourt.issue_area_codes`'
raise ValueError(msg)
if decision_direction:
if isinstance(decision_direction, compat.string_types):
decision_direction = {decision_direction}
if not all(dd in self.decision_directions
for dd in decision_direction):
msg = 'invalid `decision_direction` value; see `SupremeCourt.decision_directions`'
raise ValueError(msg)
if date_range:
date_range = self._parse_date_range(date_range)
n = 0
mode = 'rb' if compat.is_python2 else 'rt'
for line in fileio.read_json_lines(self.filename, mode=mode):
if opinion_author and line[
'maj_opinion_author'] not in opinion_author:
continue
if issue_area and line['issue_area'] not in issue_area:
continue
if decision_direction and line[
'decision_direction'] not in decision_direction:
continue
if date_range and not date_range[0] <= line[
'decision_date'] <= date_range[1]:
continue
if min_len and len(line['text']) < min_len:
continue
if text_only is True:
yield line['text']
else:
yield line
n += 1
if n == limit:
break
def _iterate(self, text_only, speaker_name=None, speaker_party=None,
chamber=None, congress=None, date_range=None, min_len=None,
limit=-1):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
# prepare filters
if speaker_name:
if isinstance(speaker_name, string_types):
speaker_name = {speaker_name}
if not all(item in self.speaker_names for item in speaker_name):
raise ValueError(
'all values in `speaker_name` must be valid; see `CapitolWords.speaker_names`')
if speaker_party:
if isinstance(speaker_party, string_types):
speaker_party = {speaker_party}
if not all(item in self.speaker_parties for item in speaker_party):
raise ValueError(
'all values in `speaker_party` must be valid; see `CapitolWords.speaker_parties`')
if chamber:
if isinstance(chamber, string_types):
chamber = {chamber}
if not all(item in self.chambers for item in chamber):
raise ValueError(
'all values in `chamber` must be valid; see `CapitolWords.chambers`')
if congress:
if isinstance(congress, int):
congress = {congress}
if not all(item in self.congresses for item in congress):
raise ValueError(
'all values in `congress` must be valid; see `CapitolWords.congresses`')
if date_range:
if not isinstance(date_range, (list, tuple)):
raise ValueError('`date_range` must be a list or tuple, not %s', type(date_range))
if not len(date_range) == 2:
raise ValueError('`date_range` must have both start and end values')
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
n = 0
mode = 'rb' if is_python2 else 'rt'
for line in read_json_lines(self.filepath, mode=mode):
if speaker_name and line['speaker_name'] not in speaker_name:
continue
if speaker_party and line['speaker_party'] not in speaker_party:
continue
if chamber and line['chamber'] not in chamber:
continue
if congress and line['congress'] not in congress:
continue
if date_range and not date_range[0] <= line['date'] <= date_range[1]:
continue
if min_len and len(line['text']) < min_len:
continue
if text_only is True:
yield line['text']
else:
yield line
n += 1
if n == limit:
break
def _iterate(self, text_only, subreddit, date_range, score_range, min_len,
limit):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
if subreddit:
if isinstance(subreddit, string_types):
subreddit = {subreddit}
elif isinstance(subreddit, (list, tuple)):
subreddit = set(subreddit)
if date_range:
if not isinstance(date_range, (list, tuple)):
msg = '`date_range` must be a list or tuple, not {}'.format(
type(date_range))
raise ValueError(msg)
if not len(date_range) == 2:
msg = '`date_range` must have both start and end values'
raise ValueError(msg)
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
if score_range:
if not isinstance(score_range, (list, tuple)):
msg = '`score_range` must be a list or tuple, not {}'.format(
type(score_range))
raise ValueError(msg)
if len(score_range) != 2:
msg = '`score_range` must have both min and max values'
raise ValueError(msg)
if not score_range[0]:
score_range = (MIN_INT, score_range[1])
if not score_range[1]:
score_range = (score_range[0], MAX_INT)
n = 0
mode = 'rb' if PY2 else 'rt' # Python 2 can't open json in text mode
for path in self.paths:
for line in read_json_lines(path, mode=mode):
if subreddit and line['subreddit'] not in subreddit:
continue
if score_range and not score_range[0] <= line[
'score'] <= score_range[1]:
continue
line['created_utc'] = self._convert_timestamp(
line.get('created_utc', ''))
if date_range and not date_range[0] <= line[
'created_utc'] <= date_range[1]:
continue
line['body'] = self._clean_content(line['body'])
if min_len and len(line['body']) < min_len:
continue
if text_only is True:
yield line['body']
else:
line['retrieved_on'] = self._convert_timestamp(
line.get('retrieved_on', ''))
yield line
n += 1
if n == limit:
break
if n == limit:
break
def _iterate(self, text_only, speaker_name, speaker_party, chamber,
congress, date_range, min_len, limit):
"""
Low-level method to iterate over the records in this dataset. Used by
:meth:`CapitolWords.texts()` and :meth:`CapitolWords.records()`.
"""
if not self.filename:
raise IOError('{} file not found'.format(self._filename))
if speaker_name:
if isinstance(speaker_name, compat.string_types):
speaker_name = {speaker_name}
if not all(item in self.speaker_names for item in speaker_name):
raise ValueError('all values in `speaker_name` must be valid; '
'see :attr:`CapitolWords.speaker_names`')
if speaker_party:
if isinstance(speaker_party, compat.string_types):
speaker_party = {speaker_party}
if not all(item in self.speaker_parties for item in speaker_party):
raise ValueError(
'all values in `speaker_party` must be valid; '
'see :attr:`CapitolWords.speaker_parties`')
if chamber:
if isinstance(chamber, compat.string_types):
chamber = {chamber}
if not all(item in self.chambers for item in chamber):
raise ValueError('all values in `chamber` must be valid; '
'see :attr:`CapitolWords.chambers`')
if congress:
if isinstance(congress, int):
congress = {congress}
if not all(item in self.congresses for item in congress):
raise ValueError('all values in `congress` must be valid; '
'see :attr:`CapitolWords.congresses`')
if date_range:
date_range = self._parse_date_range(date_range)
n = 0
mode = 'rb' if compat.is_python2 else 'rt' # TODO: check this
for line in fileio.read_json_lines(self.filename, mode=mode):
if speaker_name and line['speaker_name'] not in speaker_name:
continue
if speaker_party and line['speaker_party'] not in speaker_party:
continue
if chamber and line['chamber'] not in chamber:
continue
if congress and line['congress'] not in congress:
continue
if date_range and not date_range[0] <= line['date'] <= date_range[
1]:
continue
if min_len and len(line['text']) < min_len:
continue
if text_only is True:
yield line['text']
else:
yield line
n += 1
if n == limit:
break
def _iterate(
self,
text_only,
speaker_name=None,
speaker_party=None,
chamber=None,
congress=None,
date_range=None,
min_len=None,
limit=-1,
):
"""Note: Use `.texts()` or `.records()` to iterate over corpus data."""
# prepare filters
if speaker_name:
if isinstance(speaker_name, string_types):
speaker_name = {speaker_name}
if not all(item in self.speaker_names for item in speaker_name):
raise ValueError("all values in `speaker_name` must be valid; see `CapitolWords.speaker_names`")
if speaker_party:
if isinstance(speaker_party, string_types):
speaker_party = {speaker_party}
if not all(item in self.speaker_parties for item in speaker_party):
raise ValueError("all values in `speaker_party` must be valid; see `CapitolWords.speaker_parties`")
if chamber:
if isinstance(chamber, string_types):
chamber = {chamber}
if not all(item in self.chambers for item in chamber):
raise ValueError("all values in `chamber` must be valid; see `CapitolWords.chambers`")
if congress:
if isinstance(congress, int):
congress = {congress}
if not all(item in self.congresses for item in congress):
raise ValueError("all values in `congress` must be valid; see `CapitolWords.congresses`")
if date_range:
if not isinstance(date_range, (list, tuple)):
raise ValueError("`date_range` must be a list or tuple, not %s", type(date_range))
if not len(date_range) == 2:
raise ValueError("`date_range` must have both start and end values")
if not date_range[0]:
date_range = (MIN_DATE, date_range[1])
if not date_range[1]:
date_range = (date_range[0], MAX_DATE)
n = 0
mode = "rb" if PY2 else "rt"
for line in read_json_lines(self.filepath, mode=mode):
if speaker_name and line["speaker_name"] not in speaker_name:
continue
if speaker_party and line["speaker_party"] not in speaker_party:
continue
if chamber and line["chamber"] not in chamber:
continue
if congress and line["congress"] not in congress:
continue
if date_range and not date_range[0] <= line["date"] <= date_range[1]:
continue
if min_len and len(line["text"]) < min_len:
continue
if text_only is True:
yield line["text"]
else:
yield line
n += 1
if n == limit:
break
