def test_baseport(host, baseport, topic_range):
try:
for k in topic_range:
port = baseport + k
try:
s = socket.create_connection((host,port), 2)
s.close()
raise IOError("Socket connectable on port {0}".format(port))
except socket.error:
pass
return baseport
except IOError:
baseport = int_prompt(
"Conflict on port {0}. Enter new base port: [CURRENT: {1}]"\
.format(port, baseport))
return test_baseport(host, baseport, topic_range)
def test_port(port):
try:
host = args.host or config.get("www","host")
if host == '0.0.0.0':
host = 'localhost'
try:
s = socket.create_connection((host,port), 2)
s.close()
raise IOError("Socket connectable on port {0}".format(port))
except socket.error:
pass
return port
except IOError:
port = int_prompt(
"Conflict on port {0}. Enter new port:".format(port))
return test_port(port)
def test_port(port):
try:
host = args.host or config.get("www", "host")
if host == '0.0.0.0':
host = 'localhost'
try:
s = socket.create_connection((host, port), 2)
s.close()
raise IOError("Socket connectable on port {0}".format(port))
except socket.error:
pass
return port
except IOError:
port = int_prompt(
"Conflict on port {0}. Enter new port:".format(port))
return test_port(port)
def test_baseport(host, baseport, topic_range):
try:
for k in topic_range:
port = baseport + k
try:
s = socket.create_connection((host, port), 2)
s.close()
raise IOError(
"Socket connectable on port {0}".format(port))
except socket.error:
pass
return baseport
except IOError:
baseport = int_prompt(
"Conflict on port {0}. Enter new base port: [CURRENT: {1}]"\
.format(port, baseport))
return test_baseport(host, baseport, topic_range)
def main(args):
if args.cluster:
cluster(args.cluster, args.config_file)
return
config = topicexplorer.config.read(args.config_file)
corpus_filename = config.get("main", "corpus_file")
model_path = config.get("main", "path")
if config.getboolean("main", "sentences"):
from vsm.extensions.ldasentences import CorpusSent as Corpus
else:
from vsm.corpus import Corpus
if args.k is None:
try:
if config.get("main", "topics"):
default = ' '.join(map(str, eval(config.get("main", "topics"))))
if args.quiet:
args.k = [int(n) for n in default.split()]
else:
raise NoOptionError('main', 'topics')
except NoOptionError:
default = ' '.join(map(str, range(20, 100, 20)))
while args.k is None:
ks = input("Number of Topics [Default '{0}']: ".format(default))
try:
if ks:
args.k = [int(n) for n in ks.split()]
elif not ks.strip():
args.k = [int(n) for n in default.split()]
if args.k:
print("\nTIP: number of topics can be specified with argument '-k N N N ...':")
print(" topicexplorer train %s -k %s\n" %\
(args.config_file, ' '.join(map(str, args.k))))
except ValueError:
print("Enter valid integers, separated by spaces!")
if args.processes < 0:
import multiprocessing
args.processes = multiprocessing.cpu_count() + args.processes
print("Loading corpus... ")
corpus = Corpus.load(corpus_filename)
try:
model_pattern = config.get("main", "model_pattern")
except NoOptionError:
model_pattern = None
if (model_pattern is not None and not args.rebuild and (args.quiet or args.cont or
bool_prompt("""Existing topic models found. You can continue training or start a new model.
Do you want to continue training your existing models? """, default=True))):
from vsm.model.lda import LDA
m = LDA.load(model_pattern.format(args.k[0]),
multiprocessing=args.processes > 1,
n_proc=args.processes)
if args.iter is None and not args.quiet: # pragma: no cover
args.iter = int_prompt("Total number of training iterations:",
default=int(m.iteration * 1.5), min=m.iteration)
print("\nTIP: number of training iterations can be specified with argument '--iter N':")
print(" topicexplorer train --iter %d %s\n" % (args.iter, args.config_file))
elif args.iter is None and args.quiet: # pragma: no cover
args.iter = int(m.iteration * 1.5)
del m
# if the set changes, build some new models and continue some old ones
config_topics = eval(config.get("main", "topics"))
if args.k != config_topics:
new_models = set(args.k) - set(config_topics)
continuing_models = set(args.k) & set(config_topics)
build_models(corpus, corpus_filename, model_path,
config.get("main", "context_type"),
new_models, n_iterations=args.iter,
n_proc=args.processes, seed=args.seed,
dry_run=args.dry_run)
model_pattern = continue_training(model_pattern, continuing_models,
args.iter, n_proc=args.processes,
dry_run=args.dry_run)
else:
model_pattern = continue_training(model_pattern, args.k, args.iter,
n_proc=args.processes,
dry_run=args.dry_run)
else:
# build a new model
if args.iter is None and not args.quiet: # pragma: no cover
args.iter = int_prompt("Number of training iterations:", default=200)
print("\nTIP: number of training iterations can be specified with argument '--iter N':")
print(" topicexplorer train --iter %d %s\n" % (args.iter, args.config_file))
elif args.iter is None and args.quiet: # pragma: no cover
args.iter = 200
# TODO: if only one context_type, make it just the one context type.
ctxs = corpus.context_types
if len(ctxs) == 1:
args.context_type = ctxs[0]
else:
ctxs = sorted(ctxs, key=lambda ctx: len(corpus.view_contexts(ctx)))
if args.context_type not in ctxs:
while args.context_type not in ctxs:
contexts = ctxs[:]
contexts[0] = contexts[0].upper()
contexts = '/'.join(contexts)
args.context_type = input("Select a context type [%s] : " % contexts)
if args.context_type.strip() == '':
args.context_type = ctxs[0]
if args.context_type == ctxs[0].upper():
args.context_type = ctxs[0]
print("\nTIP: context type can be specified with argument '--context-type TYPE':")
print(" topicexplorer train --context-type %s %s\n" % (args.context_type, args.config_file))
print("\nTIP: This configuration can be automated as:")
print(" topicexplorer train %s --iter %d --context-type %s -k %s\n" %\
(args.config_file, args.iter, args.context_type,
' '.join(map(str, args.k))))
model_pattern = build_models(corpus, corpus_filename, model_path,
args.context_type, args.k,
n_iterations=args.iter,
n_proc=args.processes, seed=args.seed,
dry_run=args.dry_run)
config.set("main", "model_pattern", model_pattern)
if args.context_type:
# test for presence, since continuing doesn't require context_type
config.set("main", "context_type", args.context_type)
args.k.sort()
config.set("main", "topics", str(args.k))
if not args.dry_run:
if config.has_option("main", "cluster"):
cluster_path = config.get("main", "cluster", fallback=None)
config.remove_option("main", "cluster")
try:
if cluster_path:
os.remove(cluster_path)
except (OSError, IOError):
# fail silently on IOError
pass
with open(args.config_file, "w") as configfh:
config.write(configfh)
def main(args):
config = ConfigParser()
config.read(args.config_file)
corpus_filename = config.get("main", "corpus_file")
model_path = config.get("main", "path")
if args.k is None:
try:
if config.get("main", "topics"):
default = ' '.join(map(str, eval(config.get("main",
"topics"))))
else:
raise NoOptionError
except NoOptionError:
default = ' '.join(map(str, range(20, 100, 20)))
while args.k is None:
ks = raw_input(
"Number of Topics [Default '{0}']: ".format(default))
try:
if ks:
args.k = [int(n) for n in ks.split()]
elif not ks.strip():
args.k = [int(n) for n in default.split()]
if args.k:
print "\nTIP: number of topics can be specified with argument '-k N N N ...':"
print " vsm train %s -k %s\n" %\
(args.config_file, ' '.join(map(str, args.k)))
except ValueError:
print "Enter valid integers, separated by spaces!"
if args.processes < 0:
args.processes = multiprocessing.cpu_count() + args.processes
corpus = Corpus.load(corpus_filename)
try:
model_pattern = config.get("main", "model_pattern")
except NoOptionError:
model_pattern = None
if model_pattern is not None and\
bool_prompt("Existing models found. Continue training?", default=True):
m = LDA.load(model_pattern.format(args.k[0]),
multiprocessing=args.processes > 1,
n_proc=args.processes)
if args.iter is None:
args.iter = int_prompt("Total number of training iterations:",
default=int(m.iteration * 1.5),
min=m.iteration)
print "\nTIP: number of training iterations can be specified with argument '--iter N':"
print " vsm train --iter %d %s\n" % (args.iter,
args.config_file)
del m
# if the set changes, build some new models and continue some old ones
config_topics = eval(config.get("main", "topics"))
if args.k != config_topics:
new_models = set(args.k) - set(config_topics)
continuing_models = set(args.k) & set(config_topics)
build_models(corpus,
corpus_filename,
model_path,
config.get("main", "context_type"),
new_models,
n_iterations=args.iter,
n_proc=args.processes,
seed=args.seed)
model_pattern = continue_training(model_pattern,
continuing_models,
args.iter,
n_proc=args.processes)
else:
model_pattern = continue_training(model_pattern,
args.k,
args.iter,
n_proc=args.processes)
else:
# build a new model
if args.iter is None:
args.iter = int_prompt("Number of training iterations:",
default=200)
print "\nTIP: number of training iterations can be specified with argument '--iter N':"
print " vsm train --iter %d %s\n" % (args.iter,
args.config_file)
ctxs = corpus.context_types
ctxs = sorted(ctxs, key=lambda ctx: len(corpus.view_contexts(ctx)))
if args.context_type not in ctxs:
while args.context_type not in ctxs:
contexts = ctxs[:]
contexts[0] = contexts[0].upper()
contexts = '/'.join(contexts)
args.context_type = raw_input("Select a context type [%s] : " %
contexts)
if args.context_type.strip() == '':
args.context_type = ctxs[0]
if args.context_type == ctxs[0].upper():
args.context_type = ctxs[0]
print "\nTIP: context type can be specified with argument '--context-type TYPE':"
print " vsm train --context-type %s %s\n" % (
args.context_type, args.config_file)
print "\nTIP: This configuration can be automated as:"
print " vsm train %s --iter %d --context-type %s -k %s\n" %\
(args.config_file, args.iter, args.context_type,
' '.join(map(str, args.k)))
model_pattern = build_models(corpus,
corpus_filename,
model_path,
args.context_type,
args.k,
n_iterations=args.iter,
n_proc=args.processes,
seed=args.seed)
config.set("main", "model_pattern", model_pattern)
if args.context_type:
# test for presence, since continuing doesn't require context_type
config.set("main", "context_type", args.context_type)
args.k.sort()
config.set("main", "topics", str(args.k))
with open(args.config_file, "wb") as configfh:
config.write(configfh)
def main(args):
if args.cluster:
cluster(args.cluster, args.config_file)
return
config = topicexplorer.config.read(args.config_file)
corpus_filename = config.get("main", "corpus_file")
model_path = config.get("main", "path")
if config.getboolean("main", "sentences"):
from vsm.extensions.ldasentences import CorpusSent as Corpus
else:
from vsm.corpus import Corpus
if args.k is None:
try:
if config.get("main", "topics"):
default = ' '.join(map(str, eval(config.get("main",
"topics"))))
if args.quiet:
args.k = [int(n) for n in default.split()]
else:
raise NoOptionError('main', 'topics')
except NoOptionError:
default = ' '.join(map(str, range(20, 100, 20)))
while args.k is None:
ks = input("Number of Topics [Default '{0}']: ".format(default))
try:
if ks:
args.k = [int(n) for n in ks.split()]
elif not ks.strip():
args.k = [int(n) for n in default.split()]
if args.k:
print(
"\nTIP: number of topics can be specified with argument '-k N N N ...':"
)
print(" topicexplorer train %s -k %s\n" %\
(args.config_file, ' '.join(map(str, args.k))))
except ValueError:
print("Enter valid integers, separated by spaces!")
if args.processes < 0:
import multiprocessing
args.processes = multiprocessing.cpu_count() + args.processes
print("Loading corpus... ")
corpus = Corpus.load(corpus_filename)
try:
model_pattern = config.get("main", "model_pattern")
except NoOptionError:
model_pattern = None
if (model_pattern is not None and not args.rebuild and
(args.quiet or args.cont or bool_prompt(
"""Existing topic models found. You can continue training or start a new model.
Do you want to continue training your existing models? """,
default=True))):
from vsm.model.lda import LDA
m = LDA.load(model_pattern.format(args.k[0]),
multiprocessing=args.processes > 1,
n_proc=args.processes)
if args.iter is None and not args.quiet: # pragma: no cover
args.iter = int_prompt("Total number of training iterations:",
default=int(m.iteration * 1.5),
min=m.iteration)
print(
"\nTIP: number of training iterations can be specified with argument '--iter N':"
)
print(" topicexplorer train --iter %d %s\n" %
(args.iter, args.config_file))
elif args.iter is None and args.quiet: # pragma: no cover
args.iter = int(m.iteration * 1.5)
del m
# if the set changes, build some new models and continue some old ones
config_topics = eval(config.get("main", "topics"))
if args.k != config_topics:
new_models = set(args.k) - set(config_topics)
continuing_models = set(args.k) & set(config_topics)
build_models(corpus,
corpus_filename,
model_path,
config.get("main", "context_type"),
new_models,
n_iterations=args.iter,
n_proc=args.processes,
seed=args.seed,
dry_run=args.dry_run)
model_pattern = continue_training(model_pattern,
continuing_models,
args.iter,
n_proc=args.processes,
dry_run=args.dry_run)
else:
model_pattern = continue_training(model_pattern,
args.k,
args.iter,
n_proc=args.processes,
dry_run=args.dry_run)
else:
# build a new model
if args.iter is None and not args.quiet: # pragma: no cover
args.iter = int_prompt("Number of training iterations:",
default=200)
print(
"\nTIP: number of training iterations can be specified with argument '--iter N':"
)
print(" topicexplorer train --iter %d %s\n" %
(args.iter, args.config_file))
elif args.iter is None and args.quiet: # pragma: no cover
args.iter = 200
# TODO: if only one context_type, make it just the one context type.
ctxs = corpus.context_types
if len(ctxs) == 1:
args.context_type = ctxs[0]
else:
ctxs = sorted(ctxs, key=lambda ctx: len(corpus.view_contexts(ctx)))
if args.context_type not in ctxs:
while args.context_type not in ctxs:
contexts = ctxs[:]
contexts[0] = contexts[0].upper()
contexts = '/'.join(contexts)
args.context_type = input("Select a context type [%s] : " %
contexts)
if args.context_type.strip() == '':
args.context_type = ctxs[0]
if args.context_type == ctxs[0].upper():
args.context_type = ctxs[0]
print(
"\nTIP: context type can be specified with argument '--context-type TYPE':"
)
print(" topicexplorer train --context-type %s %s\n" %
(args.context_type, args.config_file))
print("\nTIP: This configuration can be automated as:")
print(" topicexplorer train %s --iter %d --context-type %s -k %s\n" %\
(args.config_file, args.iter, args.context_type,
' '.join(map(str, args.k))))
model_pattern = build_models(corpus,
corpus_filename,
model_path,
args.context_type,
args.k,
n_iterations=args.iter,
n_proc=args.processes,
seed=args.seed,
dry_run=args.dry_run)
config.set("main", "model_pattern", model_pattern)
if args.context_type:
# test for presence, since continuing doesn't require context_type
config.set("main", "context_type", args.context_type)
args.k.sort()
config.set("main", "topics", str(args.k))
if not args.dry_run:
if config.has_option("main", "cluster"):
cluster_path = config.get("main", "cluster", fallback=None)
config.remove_option("main", "cluster")
try:
if cluster_path:
os.remove(cluster_path)
except (OSError, IOError):
# fail silently on IOError
pass
with open(args.config_file, "w") as configfh:
config.write(configfh)
def main(args):
from vsm.corpus import Corpus
from vsm.model.lda import LDA
config = ConfigParser()
config.read(args.config_file)
corpus_filename = config.get("main", "corpus_file")
model_path = config.get("main", "path")
if args.k is None:
try:
if config.get("main", "topics"):
default = ' '.join(map(str, eval(config.get("main", "topics"))))
else:
raise NoOptionError
except NoOptionError:
default = ' '.join(map(str, range(20,100,20)))
while args.k is None:
ks = raw_input("Number of Topics [Default '{0}']: ".format(default))
try:
if ks:
args.k = [int(n) for n in ks.split()]
elif not ks.strip():
args.k = [int(n) for n in default.split()]
if args.k:
print "\nTIP: number of topics can be specified with argument '-k N N N ...':"
print " vsm train %s -k %s\n" %\
(args.config_file, ' '.join(map(str, args.k)))
except ValueError:
print "Enter valid integers, separated by spaces!"
if args.processes < 0:
args.processes = multiprocessing.cpu_count() + args.processes
print "Loading corpus... "
corpus = Corpus.load(corpus_filename)
try:
model_pattern = config.get("main", "model_pattern")
except NoOptionError:
model_pattern = None
if model_pattern is not None and\
bool_prompt("Existing models found. Continue training?", default=True):
m = LDA.load(model_pattern.format(args.k[0]),
multiprocessing=args.processes > 1,
n_proc=args.processes)
if args.iter is None:
args.iter = int_prompt("Total number of training iterations:",
default=int(m.iteration*1.5), min=m.iteration)
print "\nTIP: number of training iterations can be specified with argument '--iter N':"
print " vsm train --iter %d %s\n" % (args.iter, args.config_file)
del m
# if the set changes, build some new models and continue some old ones
config_topics = eval(config.get("main","topics"))
if args.k != config_topics :
new_models = set(args.k) - set(config_topics)
continuing_models = set(args.k) & set(config_topics)
build_models(corpus, corpus_filename, model_path,
config.get("main", "context_type"),
new_models, n_iterations=args.iter,
n_proc=args.processes, seed=args.seed)
model_pattern = continue_training(model_pattern, continuing_models,
args.iter, n_proc=args.processes)
else:
model_pattern = continue_training(model_pattern, args.k, args.iter,
n_proc=args.processes)
else:
# build a new model
if args.iter is None:
args.iter = int_prompt("Number of training iterations:", default=200)
print "\nTIP: number of training iterations can be specified with argument '--iter N':"
print " vsm train --iter %d %s\n" % (args.iter, args.config_file)
ctxs = corpus.context_types
ctxs = sorted(ctxs, key=lambda ctx: len(corpus.view_contexts(ctx)))
if args.context_type not in ctxs:
while args.context_type not in ctxs:
contexts = ctxs[:]
contexts[0] = contexts[0].upper()
contexts = '/'.join(contexts)
args.context_type = raw_input("Select a context type [%s] : " % contexts)
if args.context_type.strip() == '':
args.context_type = ctxs[0]
if args.context_type == ctxs[0].upper():
args.context_type = ctxs[0]
print "\nTIP: context type can be specified with argument '--context-type TYPE':"
print " vsm train --context-type %s %s\n" % (args.context_type, args.config_file)
print "\nTIP: This configuration can be automated as:"
print " vsm train %s --iter %d --context-type %s -k %s\n" %\
(args.config_file, args.iter, args.context_type,
' '.join(map(str, args.k)))
model_pattern = build_models(corpus, corpus_filename, model_path,
args.context_type, args.k,
n_iterations=args.iter,
n_proc=args.processes, seed=args.seed,
dry_run=args.dry_run)
config.set("main", "model_pattern", model_pattern)
if args.context_type:
# test for presence, since continuing doesn't require context_type
config.set("main", "context_type", args.context_type)
args.k.sort()
config.set("main", "topics", str(args.k))
if not args.dry_run:
with open(args.config_file, "wb") as configfh:
config.write(configfh)
