def build_index(items,
tokenizer,
outdir,
buckets=NUM_BUCKETS,
jobs=None,
chunksize=CHUNKSIZE,
sample_count=None,
sample_size=None,
term_freq=False,
line_level=False):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [
os.path.join(outdir, "bucket{0}".format(i)) for i in range(buckets)
]
for d in b_dirs:
os.mkdir(d)
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1, min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs, sample_count, sample_size,
term_freq, line_level)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
print "chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count)
print "job count: {0}".format(jobs)
if sample_count:
print "sampling-based tokenization: size {0} count {1}".format(
sample_size, sample_count)
else:
print "whole-document tokenization"
for i, keycount in enumerate(pass_tokenize_out):
print "tokenized chunk (%d/%d) [%d keys]" % (i + 1, chunk_count,
keycount)
complete = True
return b_dirs
def build_index(items,
tokenizer,
outdir,
buckets=NUM_BUCKETS,
jobs=None,
chunksize=CHUNKSIZE,
sample_count=None,
sample_size=None,
term_freq=False,
line_level=False):
global b_dirs, complete
#判断是否删除标记的文件
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [
os.path.join(outdir, "bucket{0}".format(i)) for i in range(buckets)
]
for d in b_dirs:
os.mkdir(d)
# PASS 1: 将文档分为几组
chunk_size = max(1, min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs, sample_count, sample_size,
term_freq, line_level)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
print "chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count)
print "job count: {0}".format(jobs)
if sample_count:
print "sampling-based tokenization: size {0} count {1}".format(
sample_size, sample_count)
else:
print "whole-document tokenization"
for i, keycount in enumerate(pass_tokenize_out):
print "tokenized chunk (%d/%d) [%d keys]" % (i + 1, chunk_count,
keycount)
complete = True
return b_dirs
def learn_ptc(paths, tk_nextmove, tk_output, cm, temp_path, args):
global b_dirs
num_instances = len(paths)
num_features = max(i for v in tk_output.values() for i in v) + 1
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
chunksize = min(len(paths) / (args.jobs * 2), args.chunksize)
else:
chunksize = min(len(paths) / (mp.cpu_count() * 2), args.chunksize)
# TODO: Set the output dir
b_dirs = [
tempfile.mkdtemp(prefix="train-", suffix='-bucket', dir=temp_path)
for i in range(args.buckets)
]
output_states = set(tk_output)
path_chunks = list(chunk(paths, chunksize))
pass_tokenize_arg = zip(offsets(path_chunks), path_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = sum(pass_tokenize_out)
if not SILENT:
print "wrote a total of %d keys" % write_count
pass_ptc_params = (cm, num_instances)
with MapPool(args.jobs, setup_pass_ptc, pass_ptc_params) as f:
pass_ptc_out = f(pass_ptc, b_dirs)
reads, ids, prods = zip(*pass_ptc_out)
read_count = sum(reads)
if not SILENT:
print "read a total of %d keys (%d short)" % (read_count,
write_count - read_count)
prod = np.zeros((num_features, cm.shape[1]), dtype=int)
prod[np.concatenate(ids)] = np.vstack(prods)
ptc = np.log(1 + prod) - np.log(num_features + prod.sum(0))
nb_ptc = array.array('d')
for term_dist in ptc.tolist():
nb_ptc.extend(term_dist)
return nb_ptc
def build_index(items, tokenizer, outdir, buckets=NUM_BUCKETS, jobs=None, chunksize=CHUNKSIZE, sample_count=None, sample_size=None, term_freq=False):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [ os.path.join(outdir,"bucket{0}".format(i)) for i in range(buckets) ]
for d in b_dirs:
os.mkdir(d)
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1,min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs, sample_count, sample_size, term_freq)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
if not SILENT:
print "chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count)
print "job count: {0}".format(jobs)
if sample_count:
if not SILENT:
print "sampling-based tokenization: size {0} count {1}".format(sample_size, sample_count)
else:
if not SILENT:
print "whole-document tokenization"
for i, keycount in enumerate(pass_tokenize_out):
if not SILENT:
print "tokenized chunk (%d/%d) [%d keys]" % (i+1,chunk_count, keycount)
complete = True
return b_dirs
def learn_ptc(paths, tk_nextmove, tk_output, cm, temp_path, args):
global b_dirs
num_instances = len(paths)
num_features = max( i for v in tk_output.values() for i in v) + 1
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
chunksize = min(len(paths) / (args.jobs*2), args.chunksize)
else:
chunksize = min(len(paths) / (mp.cpu_count()*2), args.chunksize)
# TODO: Set the output dir
b_dirs = [ tempfile.mkdtemp(prefix="train-",suffix='-bucket', dir=temp_path) for i in range(args.buckets) ]
output_states = set(tk_output)
path_chunks = list(chunk(paths, chunksize))
pass_tokenize_arg = zip(offsets(path_chunks), path_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = sum(pass_tokenize_out)
if not SILENT:
print "wrote a total of %d keys" % write_count
pass_ptc_params = (cm, num_instances)
with MapPool(args.jobs, setup_pass_ptc, pass_ptc_params) as f:
pass_ptc_out = f(pass_ptc, b_dirs)
reads, ids, prods = zip(*pass_ptc_out)
read_count = sum(reads)
if not SILENT:
print "read a total of %d keys (%d short)" % (read_count, write_count - read_count)
prod = np.zeros((num_features, cm.shape[1]), dtype=int)
prod[np.concatenate(ids)] = np.vstack(prods)
ptc = np.log(1 + prod) - np.log(num_features + prod.sum(0))
nb_ptc = array.array('d')
for term_dist in ptc.tolist():
nb_ptc.extend(term_dist)
return nb_ptc
def build_index(items,
tokenizer,
outdir,
buckets=NUM_BUCKETS,
jobs=None,
chunksize=CHUNKSIZE):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [
tempfile.mkdtemp(prefix="tokenize-",
suffix='-{0}'.format(tokenizer.__class__.__name__),
dir=outdir) for i in range(buckets)
]
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1, min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
print "chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count)
print "job count: {0}".format(jobs)
for i, keycount in enumerate(pass_tokenize_out):
print "tokenized chunk (%d/%d) [%d keys]" % (i + 1, chunk_count,
keycount)
complete = True
return b_dirs
def build_index(items, tokenizer, outdir, buckets=NUM_BUCKETS, jobs=None, chunksize=CHUNKSIZE, sample_count=None, sample_size=None):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [ tempfile.mkdtemp(prefix="tokenize-",suffix='-{0}'.format(tokenizer.__class__.__name__), dir=outdir) for i in range(buckets) ]
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1,min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs, sample_count, sample_size)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
logger.info("chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count))
logger.info("job count: {0}".format(jobs))
if sample_count:
logger.info("sampling-based tokenization: size {0} count {1}".format(sample_size, sample_count))
else:
logger.info("whole-document tokenization")
total_bytes = 0
for i, chunk_bytes in enumerate(pass_tokenize_out):
logger.debug("tokenized chunk (%d/%d) [%d bytes]" % (i+1,chunk_count, chunk_bytes))
total_bytes += chunk_bytes
logger.info("tokenized a total of {0} MB".format(total_bytes / 1024 / 1024))
complete = True
return b_dirs
def learn_ftc(paths, tk_nextmove, tk_output, cm, temp_path, args):
global b_dirs
num_instances = len(paths)
num_features = max(i for v in tk_output.values() for i in v) + 1
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
chunksize = min(len(paths) / (args.jobs * 2), args.chunksize)
else:
chunksize = min(len(paths) / (mp.cpu_count() * 2), args.chunksize)
# TODO: Set the output dir
b_dirs = [
tempfile.mkdtemp(prefix="train-", suffix='-bucket', dir=temp_path)
for i in range(args.buckets)
]
output_states = set(tk_output)
path_chunks = list(chunk(paths, chunksize))
pass_tokenize_arg = zip(offsets(path_chunks), path_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = sum(pass_tokenize_out)
logger.info("wrote a total of %d keys", write_count)
# TODO: Report on the progress of this pass
pass_ftc_params = (cm, num_instances)
with MapPool(args.jobs, setup_pass_ftc, pass_ftc_params) as f:
pass_ftc_out = f(pass_ftc, b_dirs)
reads, ids, prods = zip(*pass_ftc_out)
read_count = sum(reads)
logger.info("read a total of %d keys (%d short)", read_count,
write_count - read_count)
# Re-order the weights into a single ndarray
term_lang_counts = np.zeros((num_features, cm.shape[1]), dtype=int)
term_lang_counts[np.concatenate(ids)] = np.vstack(prods)
return term_lang_counts
def learn_ftc(paths, tk_nextmove, tk_output, cm, temp_path, args):
global b_dirs
num_instances = len(paths)
num_features = max( i for v in tk_output.values() for i in v) + 1
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
chunksize = min(len(paths) / (args.jobs*2), args.chunksize)
else:
chunksize = min(len(paths) / (mp.cpu_count()*2), args.chunksize)
# TODO: Set the output dir
b_dirs = [ tempfile.mkdtemp(prefix="train-",suffix='-bucket', dir=temp_path) for i in range(args.buckets) ]
output_states = set(tk_output)
path_chunks = list(chunk(paths, chunksize))
pass_tokenize_arg = zip(offsets(path_chunks), path_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = sum(pass_tokenize_out)
logger.info("wrote a total of %d keys", write_count)
# TODO: Report on the progress of this pass
pass_ftc_params = (cm, num_instances)
with MapPool(args.jobs, setup_pass_ftc, pass_ftc_params) as f:
pass_ftc_out = f(pass_ftc, b_dirs)
reads, ids, prods = zip(*pass_ftc_out)
read_count = sum(reads)
logger.info("read a total of %d keys (%d short)", read_count, write_count - read_count)
# Re-order the weights into a single ndarray
term_lang_counts = np.zeros((num_features, cm.shape[1]), dtype=int)
term_lang_counts[np.concatenate(ids)] = np.vstack(prods)
return term_lang_counts
def build_index(items, tokenizer, outdir, buckets=NUM_BUCKETS, jobs=None, chunksize=CHUNKSIZE):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [ tempfile.mkdtemp(prefix="tokenize-",suffix='-{0}'.format(tokenizer.__class__.__name__), dir=outdir) for i in range(buckets) ]
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1,min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
print "chunk size: {0} ({1} chunks)".format(chunk_size, chunk_count)
print "job count: {0}".format(jobs)
for i, keycount in enumerate(pass_tokenize_out):
print "tokenized chunk (%d/%d) [%d keys]" % (i+1,chunk_count, keycount)
complete = True
return b_dirs
def learn_nb_params(items, num_langs, tk_nextmove, tk_output, temp_path, args):
"""
@param items label, path pairs
"""
global outdir
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
tasks = args.jobs * 2
else:
tasks = mp.cpu_count() * 2
# Ensure chunksize of at least 1, but not exceeding specified chunksize
chunksize = max(1, min(len(items) / tasks, args.chunksize))
outdir = tempfile.mkdtemp(prefix="NBtrain-",suffix='-buckets', dir=temp_path)
b_dirs = [ os.path.join(outdir,"bucket{0}".format(i)) for i in range(args.buckets) ]
for d in b_dirs:
os.mkdir(d)
output_states = set(tk_output)
# Divide all the items to be processed into chunks, and enumerate each chunk.
item_chunks = list(chunk(items, chunksize))
pass_tokenize_arg = enumerate(item_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs, args.line)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = 0
chunk_sizes = {}
labels = []
for chunk_id, doc_count, writes, _labels in pass_tokenize_out:
write_count += writes
chunk_sizes[chunk_id] = doc_count
labels.extend(_labels)
print "wrote a total of %d keys" % write_count
num_instances = sum(chunk_sizes.values())
print "processed a total of %d instances" % num_instances
chunk_offsets = {}
for i in range(len(chunk_sizes)):
chunk_offsets[i] = sum(chunk_sizes[x] for x in range(i))
print " offset for chunk {0} is {1}".format(i, chunk_offsets[i])
pass_fm_params = (num_instances, chunk_offsets)
with MapPool(args.jobs, setup_pass_fm, pass_fm_params) as f:
pass_fm_out = f(pass_fm, b_dirs)
reads, ids, fms = zip(*pass_fm_out)
read_count = sum(reads)
print "read a total of %d keys (%d short)" % (read_count, write_count - read_count)
num_features = max( i for v in tk_output.values() for i in v) + 1
fm = np.zeros((num_features, num_instances), dtype=int)
fm[np.concatenate(ids)] = np.vstack(fms)
print "have {} labels".format(len(labels))
cm = np.zeros((num_instances, num_langs), dtype='bool')
for doc_id, lang_id in enumerate(labels):
cm[doc_id, lang_id] = True
# This is where the smoothing occurs
prod = np.dot(fm, cm)
ptc = np.log(1 + prod) - np.log(num_features + prod.sum(0))
nb_ptc = array.array('d')
for term_dist in ptc.tolist():
nb_ptc.extend(term_dist)
pc = np.log(cm.sum(0))
nb_pc = array.array('d', pc)
return nb_pc, nb_ptc
def learn_nb_params(items, num_langs, tk_nextmove, tk_output, temp_path, args):
"""
@param items label, path pairs
"""
global outdir
print "learning NB parameters on {} items".format(len(items))
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
tasks = args.jobs * 2
else:
tasks = mp.cpu_count() * 2
# Ensure chunksize of at least 1, but not exceeding specified chunksize
chunksize = max(1, min(len(items) / tasks, args.chunksize))
outdir = tempfile.mkdtemp(prefix="NBtrain-",
suffix='-buckets',
dir=temp_path)
b_dirs = [
os.path.join(outdir, "bucket{0}".format(i))
for i in range(args.buckets)
]
for d in b_dirs:
os.mkdir(d)
output_states = set(tk_output)
# Divide all the items to be processed into chunks, and enumerate each chunk.
item_chunks = list(chunk(items, chunksize))
num_chunks = len(item_chunks)
print "about to tokenize {} chunks".format(num_chunks)
pass_tokenize_arg = enumerate(item_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs,
args.line)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = 0
chunk_sizes = {}
chunk_labels = []
for i, (chunk_id, doc_count, writes,
labels) in enumerate(pass_tokenize_out):
write_count += writes
chunk_sizes[chunk_id] = doc_count
chunk_labels.append((chunk_id, labels))
print "processed chunk ID:{0} ({1}/{2}) [{3} keys]".format(
chunk_id, i + 1, num_chunks, writes)
print "wrote a total of %d keys" % write_count
num_instances = sum(chunk_sizes.values())
print "processed a total of %d instances" % num_instances
chunk_offsets = {}
for i in range(len(chunk_sizes)):
chunk_offsets[i] = sum(chunk_sizes[x] for x in range(i))
# Build CM based on re-ordeing chunk
cm = np.zeros((num_instances, num_langs), dtype='bool')
for chunk_id, chunk_label in chunk_labels:
for doc_id, lang_id in enumerate(chunk_label):
index = doc_id + chunk_offsets[chunk_id]
cm[index, lang_id] = True
pass_ptc_params = (cm, num_instances, chunk_offsets)
with MapPool(args.jobs, setup_pass_ptc, pass_ptc_params) as f:
pass_ptc_out = f(pass_ptc, b_dirs)
def pass_ptc_progress():
for i, v in enumerate(pass_ptc_out):
yield v
print "processed chunk ({0}/{1})".format(i + 1, len(b_dirs))
reads, ids, prods = zip(*pass_ptc_progress())
read_count = sum(reads)
print "read a total of %d keys (%d short)" % (read_count,
write_count - read_count)
num_features = max(i for v in tk_output.values() for i in v) + 1
prod = np.zeros((num_features, cm.shape[1]), dtype=int)
prod[np.concatenate(ids)] = np.vstack(prods)
# This is where the smoothing occurs
ptc = np.log(1 + prod) - np.log(num_features + prod.sum(0))
nb_ptc = array.array('d')
for term_dist in ptc.tolist():
nb_ptc.extend(term_dist)
pc = np.log(cm.sum(0))
nb_pc = array.array('d', pc)
return nb_pc, nb_ptc
def build_index(items,
tokenizer,
outdir,
buckets=NUM_BUCKETS,
jobs=None,
chunksize=CHUNKSIZE,
sample_count=None,
sample_size=None):
"""
@param items a list of (domain, language, path) tuples
"""
global b_dirs, complete
# Our exitfunc uses this to know whether to delete the tokenized files
complete = False
if jobs is None:
jobs = mp.cpu_count() + 4
b_dirs = [
tempfile.mkdtemp(prefix="tokenize-",
suffix='-{0}'.format(tokenizer.__class__.__name__),
dir=outdir) for i in range(buckets)
]
# PASS 1: Tokenize documents into sets of terms
# If there are few items, make the chunk size such that each job
# will have 2 chunks
chunk_size = max(1, min(len(items) / (jobs * 2), chunksize))
item_chunks = list(chunk(items, chunk_size))
pass_tokenize_globals = (tokenizer, b_dirs, sample_count, sample_size)
with MapPool(jobs, setup_pass_tokenize, pass_tokenize_globals) as f:
pass_tokenize_out = f(pass_tokenize, item_chunks)
doc_count = defaultdict(int)
chunk_count = len(item_chunks)
logger.info("chunk size: {0} ({1} chunks)".format(
chunk_size, chunk_count))
logger.info("job count: {0}".format(jobs))
if sample_count:
logger.info(
"sampling-based tokenization: size {0} count {1}".format(
sample_size, sample_count))
else:
logger.info("whole-document tokenization")
total_bytes = 0
for i, chunk_bytes in enumerate(pass_tokenize_out):
logger.debug("tokenized chunk (%d/%d) [%d bytes]" %
(i + 1, chunk_count, chunk_bytes))
total_bytes += chunk_bytes
logger.info("tokenized a total of {0} MB".format(total_bytes / 1024 /
1024))
complete = True
return b_dirs
def learn_nb_params(items, num_langs, tk_nextmove, tk_output, temp_path, args):
"""
@param items label, path pairs
"""
global outdir
print "learning NB parameters on {} items".format(len(items))
# Generate the feature map
nm_arr = mp.Array('i', tk_nextmove, lock=False)
if args.jobs:
tasks = args.jobs * 2
else:
tasks = mp.cpu_count() * 2
# Ensure chunksize of at least 1, but not exceeding specified chunksize
chunksize = max(1, min(len(items) / tasks, args.chunksize))
outdir = tempfile.mkdtemp(prefix="NBtrain-",suffix='-buckets', dir=temp_path)
b_dirs = [ os.path.join(outdir,"bucket{0}".format(i)) for i in range(args.buckets) ]
for d in b_dirs:
os.mkdir(d)
output_states = set(tk_output)
# Divide all the items to be processed into chunks, and enumerate each chunk.
item_chunks = list(chunk(items, chunksize))
num_chunks = len(item_chunks)
print "about to tokenize {} chunks".format(num_chunks)
pass_tokenize_arg = enumerate(item_chunks)
pass_tokenize_params = (nm_arr, output_states, tk_output, b_dirs, args.line)
with MapPool(args.jobs, setup_pass_tokenize, pass_tokenize_params) as f:
pass_tokenize_out = f(pass_tokenize, pass_tokenize_arg)
write_count = 0
chunk_sizes = {}
chunk_labels = []
for i, (chunk_id, doc_count, writes, labels) in enumerate(pass_tokenize_out):
write_count += writes
chunk_sizes[chunk_id] = doc_count
chunk_labels.append((chunk_id, labels))
print "processed chunk ID:{0} ({1}/{2}) [{3} keys]".format(chunk_id, i+1, num_chunks, writes)
print "wrote a total of %d keys" % write_count
num_instances = sum(chunk_sizes.values())
print "processed a total of %d instances" % num_instances
chunk_offsets = {}
for i in range(len(chunk_sizes)):
chunk_offsets[i] = sum(chunk_sizes[x] for x in range(i))
# Build CM based on re-ordeing chunk
cm = np.zeros((num_instances, num_langs), dtype='bool')
for chunk_id, chunk_label in chunk_labels:
for doc_id, lang_id in enumerate(chunk_label):
index = doc_id + chunk_offsets[chunk_id]
cm[index, lang_id] = True
pass_ptc_params = (cm, num_instances, chunk_offsets)
with MapPool(args.jobs, setup_pass_ptc, pass_ptc_params) as f:
pass_ptc_out = f(pass_ptc, b_dirs)
def pass_ptc_progress():
for i,v in enumerate(pass_ptc_out):
yield v
print "processed chunk ({0}/{1})".format(i+1, len(b_dirs))
reads, ids, prods = zip(*pass_ptc_progress())
read_count = sum(reads)
print "read a total of %d keys (%d short)" % (read_count, write_count - read_count)
num_features = max( i for v in tk_output.values() for i in v) + 1
prod = np.zeros((num_features, cm.shape[1]), dtype=int)
prod[np.concatenate(ids)] = np.vstack(prods)
# This is where the smoothing occurs
ptc = np.log(1 + prod) - np.log(num_features + prod.sum(0))
nb_ptc = array.array('d')
for term_dist in ptc.tolist():
nb_ptc.extend(term_dist)
pc = np.log(cm.sum(0))
nb_pc = array.array('d', pc)
return nb_pc, nb_ptc
