def cooccurrence(
pool=None,
context=('c', 'context.csv', 'The file with context words.'),
targets=('t', 'targets.csv', 'The file with target words.'),
input_dir=(
'i',
local('./downloads/google_ngrams/5_cooccurrence'),
'The path to the directory with the co-occurence.',
),
output=('o', 'matrix.h5', 'The output matrix file.'),
):
"""Build the co-occurrence matrix."""
file_names = input_dir.listdir(sort=True)
pieces = pool.map(load_cooccurrence, ((f, targets, context) for f in file_names))
# Get rid of empty frames
pieces = list(filter(len, pieces))
while len(pieces) > 1:
logger.info('Pairs left %s', len(pieces))
if divmod(len(pieces), 2)[1]:
odd = [pieces.pop()]
else:
odd = []
pieces = list(pool.map(group_sum, get_pairs(pieces))) + odd
matrix, = pieces
write_space(output, context, targets, matrix)
def cooccurrence(
pool=None,
context=('c', 'context.csv', 'The file with context words.'),
targets=('t', 'targets.csv', 'The file with target words.'),
input_dir=(
'i',
local('./downloads/google_ngrams/5_cooccurrence'),
'The path to the directory with the co-occurence.',
),
output=('o', 'matrix.h5', 'The output matrix file.'),
):
"""Build the co-occurrence matrix."""
file_names = input_dir.listdir(sort=True)
pieces = pool.map(load_cooccurrence,
((f, targets, context) for f in file_names))
# Get rid of empty frames
pieces = list(filter(len, pieces))
while len(pieces) > 1:
logger.info('Pairs left %s', len(pieces))
if divmod(len(pieces), 2)[1]:
odd = [pieces.pop()]
else:
odd = []
pieces = list(pool.map(group_sum, get_pairs(pieces))) + odd
matrix, = pieces
write_space(output, context, targets, matrix)
def cooccurrence(
corpus,
execnet_hub,
targets,
context,
paths_progress_iter,
output=('o', 'space.h5', 'The output space file.'),
):
"""Build the co-occurrence matrix."""
if targets.index.nlevels > 1:
targets.sortlevel(inplace=True)
if context.index.nlevels > 1:
context.sortlevel(inplace=True)
def init(channel):
channel.send(
(
'data',
pickle.dumps(
{
'kwargs': {
'targets': targets,
'context': context,
},
'instance': corpus,
'folder_name': 'cooccurrence',
},
)
)
)
results = execnet_hub.run(
remote_func=sum_folder,
iterable=paths_progress_iter,
init_func=init,
)
results = ([r] for r in results if r is not None)
result = next(results)[0]
for i, chunk in enumerate(chunked(results, 100)):
logger.info('Received result chunk #%s.', i)
chunked_result = [c[0] for c in chunk]
with Timer() as timed:
result = pd.concat(
chunked_result + [result],
copy=False,
).groupby(level=result.index.names).sum()
logger.info(
'Computed the result by merging a chunk of received results and the result in %.2f seconds.',
timed.elapsed,
)
result = result.to_frame('count')
result.reset_index(inplace=True)
write_space(output, context, targets, result)
def cooccurrence(
corpus,
execnet_hub,
targets,
context,
paths_progress_iter,
output=('o', 'space.h5', 'The output space file.'),
):
"""Build the co-occurrence matrix."""
if targets.index.nlevels > 1:
targets.sortlevel(inplace=True)
if context.index.nlevels > 1:
context.sortlevel(inplace=True)
def init(channel):
channel.send(
(
'data',
pickle.dumps(
{
'kwargs': {
'targets': targets,
'context': context,
},
'instance': corpus,
'folder_name': 'cooccurrence',
},
)
)
)
results = execnet_hub.run(
remote_func=sum_folder,
iterable=paths_progress_iter,
init_func=init,
)
results = ([r] for r in results if r is not None)
result = next(results)[0]
for i, chunk in enumerate(chunked(results, 100)):
logger.info('Received result chunk #%s.', i)
chunked_result = [c[0] for c in chunk]
with Timer() as timed:
result = pd.concat(
chunked_result + [result],
copy=False,
).groupby(level=result.index.names).sum()
logger.info(
'Computed the result by merging a chunk of received results and the result in %.2f seconds.',
timed.elapsed,
)
result = result.to_frame('count')
result.reset_index(inplace=True)
write_space(output, context, targets, result)
def write(self, file_name):
"""Write the vector space to a file."""
coo = coo_matrix(self.matrix)
matrix = pd.DataFrame({
'count': coo.data,
'id_target': coo.row,
'id_context': coo.col,
}).set_index(['id_target', 'id_context'])
write_space(file_name, self.column_labels, self.row_labels, matrix)
def write(self, file_name):
"""Write the vector space to a file."""
coo = coo_matrix(self.matrix)
matrix = pd.DataFrame(
{
'count': coo.data,
'id_target': coo.row,
'id_context': coo.col,
}
).set_index(['id_target', 'id_context'])
write_space(file_name, self.column_labels, self.row_labels, matrix)
def cooccurrence(
bnc,
pool,
targets,
context,
window_size=('', 5, 'Window size.'),
chunk_size=('', 7, 'Length of the chunk at the reduce stage.'),
stem=('', False, 'Use word stems instead of word strings.'),
output=('o', 'matrix.h5', 'The output matrix file.'),
):
"""Build the co-occurrence matrix."""
records = Counter()
all_fileids = Bar(
'Reading BNC',
max=len(bnc.fileids()),
suffix='%(index)d/%(max)d, elapsed: %(elapsed_td)s',
).iter(bnc.fileids())
for fileids_chunk in chunked(all_fileids, 100):
counters = pool.imap_unordered(
bnc_cooccurrence,
((bnc.root, fileids, window_size, stem, targets, context)
for fileids in fileids_chunk),
)
records += sum_counters(counters, pool=pool, chunk_size=chunk_size)
logger.debug('There are %d co-occurrence records so far.',
len(records))
matrix = pd.DataFrame(
([t, c, n] for (t, c), n in records.items()),
columns=('id_target', 'id_context', 'count'),
)
matrix.set_index(['id_target', 'id_context'], inplace=True)
write_space(output, context, targets, matrix)
