def main():
usage = "%prog [options]"
description = "Runs a suite of small parsing experiments to try different beam settings"
parser = OptionParser(usage=usage, description=description)
options, arguments = parser.parse_args()
cmd_dir = os.path.abspath(os.path.join("..", ".."))
parser = os.path.join(cmd_dir, "jazzparser")
result_eval = os.path.join(cmd_dir, "analysis", "result_eval.py")
result_eval_dir = os.path.join(cmd_dir, "analysis")
# Try all combinations of threshold and maxarc settings
settings = [(threshold,maxarc) for threshold in [0.5, 0.1, 0.01, 0.001] \
for maxarc in [5, 10, 15, 20]]
# Don't try different thresholds for maxarc=1: they're all the same
settings.append((0.1, 1))
# Open a CSV file to write the results to
with open("test_suite.csv", "w") as result_file:
results = csv.writer(result_file)
results.writerow(["Threshold", "Maxarc", "Dep rec"])
for threshold,maxarc in settings:
print "\n#####################################################"
print "### Threshold %s, maxarc %d ###" % (threshold, maxarc)
# Build a config file string for these settings
options = "%%%% DEF threshold %s\n" % threshold
options += "%%%% DEF maxarc %d\n" % maxarc
conf = ConfigFile.from_string(options+BASE_CONFIG)
# Run the parser
retcode = call([parser]+conf.get_strings(),
cwd=cmd_dir, stderr=STDOUT)
if retcode:
print "Parse failed"
# Don't bother continuing with the others
return 1
# Find out where the output was being put
output_dir = dict(conf.options)['output']
files = os.path.join(output_dir, "*.res")
# Evaluate all the results files in that directory
eval_proc = Popen([result_eval, files, "--mopt output=f",
"-m deprec", "-q"],
cwd=result_eval_dir, stdout=PIPE)
eval_out = eval_proc.stdout.read()
f_score = eval_out.rstrip().rstrip("%")
# Write the result out to the summary file
results.writerow(["%s" % threshold,
"%d" % maxarc,
f_score])
# Flush the file object so each result appears immediately
result_file.flush()
def main():
usage = "%prog [options]"
description = "Runs a suite of small parsing experiments to try different beam settings"
parser = OptionParser(usage=usage, description=description)
options, arguments = parser.parse_args()
cmd_dir = os.path.abspath(os.path.join("..", ".."))
parser = os.path.join(cmd_dir, "jazzparser")
result_eval = os.path.join(cmd_dir, "analysis", "result_eval.py")
result_eval_dir = os.path.join(cmd_dir, "analysis")
# Try all combinations of threshold and maxarc settings
settings = [(threshold,maxarc) for threshold in [0.5, 0.1, 0.01, 0.001] \
for maxarc in [5, 10, 15, 20]]
# Don't try different thresholds for maxarc=1: they're all the same
settings.append((0.1, 1))
# Open a CSV file to write the results to
with open("test_suite.csv", "w") as result_file:
results = csv.writer(result_file)
results.writerow(["Threshold", "Maxarc", "Dep rec"])
for threshold, maxarc in settings:
print "\n#####################################################"
print "### Threshold %s, maxarc %d ###" % (threshold, maxarc)
# Build a config file string for these settings
options = "%%%% DEF threshold %s\n" % threshold
options += "%%%% DEF maxarc %d\n" % maxarc
conf = ConfigFile.from_string(options + BASE_CONFIG)
# Run the parser
retcode = call([parser] + conf.get_strings(),
cwd=cmd_dir,
stderr=STDOUT)
if retcode:
print "Parse failed"
# Don't bother continuing with the others
return 1
# Find out where the output was being put
output_dir = dict(conf.options)['output']
files = os.path.join(output_dir, "*.res")
# Evaluate all the results files in that directory
eval_proc = Popen(
[result_eval, files, "--mopt output=f", "-m deprec", "-q"],
cwd=result_eval_dir,
stdout=PIPE)
eval_out = eval_proc.stdout.read()
f_score = eval_out.rstrip().rstrip("%")
# Write the result out to the summary file
results.writerow(["%s" % threshold, "%d" % maxarc, f_score])
# Flush the file object so each result appears immediately
result_file.flush()
def main():
usage = "%prog [options]"
description = "Trains a suite of ngram models and tests them all"
parser = OptionParser(usage=usage, description=description)
parser.add_option('-n', '--no-train', dest="no_train", action="store_true", help="don't train the models. Only do this if you've previously used this script to train all the models")
parser.add_option('--train', '--only-train', dest="only_train", action="store_true", help="only train the models, don't do the experiments")
parser.add_option('--bt', '--bigram-trigram', dest="bigram_trigram", action="store_true", help="only include bigram and trigram models")
parser.add_option('-t', '--trigram', dest="trigram", action="store_true", help="only include trigram models")
parser.add_option('--wb', '--witten-bell', dest="witten_bell", action="store_true", help="only use witten-bell smoothing (skip laplace)")
parser.add_option('--lap', '--laplace', dest="laplace", action="store_true", help="only use laplace smoothing (skip witten-bell)")
parser.add_option('-v', '--viterbi', dest="viterbi", action="store_true", help="use Viterbi decoding")
parser.add_option('-4', '--4grams', dest="fourgrams", action="store_true", help="run experiments for 4-gram models")
parser.add_option('-c', '--cutoff', dest="cutoff", action="store", type="int", help="custom cutoff to use, instead of trying several")
parser.add_option('--gt', '--good-turing', dest="good_turing", action="store_true", help="only use Good-Turing smoothing (not usually included)")
options, arguments = parser.parse_args()
cmd_dir = os.path.abspath("..")
train_cmd = "./train.py"
tageval_cmd = "./tageval.py"
if options.bigram_trigram:
orders = [2, 3]
elif options.trigram:
orders = [3]
elif options.fourgrams:
orders = [4]
else:
orders = [1, 2, 3]
if options.witten_bell:
smoothings = [("witten-bell", "wb")]
elif options.laplace:
smoothings = [("laplace", "lap")]
elif options.good_turing:
smoothings = [("simple-good-turing", "gt")]
else:
smoothings = [("witten-bell", "wb"), ("laplace", "lap")]
if options.cutoff is None:
cutoffs = [0, 2, 5]
else:
cutoffs = [options.cutoff]
# Open a CSV file to write the results to
with open("test_suite.csv", "w") as result_file:
results = csv.writer(result_file)
results.writerow(["Order", "Cutoff", "Smoothing", "Entropy", "Agreement"])
for model_order in orders:
for cutoff in cutoffs:
for smoothing,smoothing_short in smoothings:
#for chord_map in ["none", "small", "big"]:
print "\n#####################################################"
print "### Order %d, cutoff %d, smoothing %s ###" % (model_order, cutoff, smoothing)
# Build a unique name for the model
model_name = "suite_n%d_c%d_%s" % (model_order, cutoff, smoothing_short)
# Train the model
if not options.no_train:
# Prepare options to train the model
model_options = "n=%d:cutoff=%d:backoff=%d:estimator=%s" % \
(model_order, cutoff, model_order-1, smoothing)
training_opts = BASE_TRAINING_OPTIONS + \
"opts = %s\n%%%% ARG 1 %s" % (model_options, model_name)
# Turn these nice option specifications into command-line args
conf = ConfigFile.from_string(training_opts)
# Train this model
#train_output = check_output([train_cmd]+conf.get_strings(), cwd=cmd_dir)
train_proc = Popen([train_cmd]+conf.get_strings(),
cwd=cmd_dir, stdout=PIPE, stderr=STDOUT)
output_proc(train_proc)
if not options.only_train:
# Entropy doesn't tell us much for Viterbi decoding
if not options.viterbi:
# Test the model's entropy
print "### Entropy ###"
entropy_opts = BASE_ENTROPY_OPTIONS + "%%%% ARG 1 %s" % model_name
conf = ConfigFile.from_string(entropy_opts)
entropy_proc = Popen([tageval_cmd]+conf.get_strings(),
cwd=cmd_dir, stdout=PIPE, stderr=STDOUT)
# Output as we go
output = output_proc(entropy_proc)
# Get the last line and pull out the entropy value
last_line = output.strip("\n").rpartition("\n")[2]
entropy = float(last_line.split()[0])
else:
entropy = 0.0
# Test the model's top tag accuracy
print "\n### Agreement ###"
accuracy_opts = BASE_ACCURACY_OPTIONS + "%%%% ARG 1 %s" % model_name
if options.viterbi:
accuracy_opts += "\ntopt = decode=viterbi"
conf = ConfigFile.from_string(accuracy_opts)
accuracy_proc = Popen([tageval_cmd]+conf.get_strings(),
cwd=cmd_dir, stdout=PIPE, stderr=STDOUT)
# Output as we go
output = output_proc(accuracy_proc)
# Get the last line and pull out the agreement value
last_line = output.strip("\n").rpartition("\n")[2]
agreement = float(last_line.split()[-1].strip("()%"))
results.writerow(["%d" % model_order,
"%d" % cutoff,
"%s" % smoothing,
"%f" % entropy,
"%f" % agreement])
# Flush the file object so each result appears in the
# file immediately
result_file.flush()
