def __init__(self, filename='awesome.model', type=libml.ALL):
model_directory = os.path.dirname(filename)
if model_directory != "":
helper.mkpath(model_directory)
self.filename = os.path.realpath(filename)
self.type = type
self.vocab = {}
self.enabled_features = Model.sentence_features | Model.word_features
def __init__(self):
# Ensure cache dir exists
cache_dir = os.path.join(os.getenv('CLINER_DIR'), 'caches')
helper.mkpath(cache_dir)
# Read data
self.filename = os.path.join(cache_dir, 'url.cache')
try:
self.cache = load_pickled_obj(self.filename)
except IOError:
self.cache = {}
self.new = {}
def __init__(self):
# Ensure cache dir exists
cache_dir = os.path.join(os.getenv('CLINER_DIR'), 'caches')
helper.mkpath(cache_dir)
# Read data
self.filename = os.path.join(cache_dir, 'url.cache')
try:
self.cache = load_pickled_obj(self.filename)
except IOError:
self.cache = {}
self.new = {}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i",
dest = "input",
help = "The input files to predict",
default = os.path.join(os.getenv('CLINER_DIR'), 'data/test_data/*')
)
parser.add_argument("-o",
dest = "output",
help = "The directory to write the output",
default = os.path.join(os.getenv('CLINER_DIR'), 'data/test_predictions')
)
parser.add_argument("-m",
dest = "model",
help = "The model to use for prediction",
default = os.path.join(os.getenv('CLINER_DIR'), 'models/run.model')
)
parser.add_argument("-f",
dest = "format",
help = "Data format ( " + ' | '.join(Note.supportedFormats()) + " )",
default = 'i2b2'
)
parser.add_argument("-crf",
dest = "with_crf",
help = "Specify where to find crfsuite",
default = None
)
args = parser.parse_args()
# Parse arguments
files = glob.glob(args.input)
helper.mkpath(args.output)
format = args.format
# Predict
predict(files, args.model, args.output, format=format)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i",
dest = "input",
help = "The input files to predict",
default = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/test_data/*')
)
parser.add_argument("-o",
dest = "output",
help = "The directory to write the output",
default = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/test_predictions')
)
parser.add_argument("-m",
dest = "model",
help = "The model to use for prediction",
default = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/awesome.model')
)
parser.add_argument("--no-svm",
dest = "no_svm",
action = "store_true",
help = "Disable SVM model generation",
)
parser.add_argument("--no-lin",
dest = "no_lin",
action = "store_true",
help = "Disable LIN model generation",
)
parser.add_argument("--no-crf",
dest = "no_crf",
action = "store_true",
help = "Disable CRF model generation",
)
args = parser.parse_args()
# Locate the test files
files = glob.glob(args.input)
# Load a model and make a prediction for each file
path = args.output
helper.mkpath(args.output)
model = Model.load(args.model)
if args.no_svm:
model.type &= ~libml.SVM
if args.no_lin:
model.type &= ~libml.LIN
if args.no_crf:
model.type &= ~libml.CRF
for txt in files:
data = read_txt(txt)
labels = model.predict(data)
con = os.path.split(txt)[-1]
con = con[:-3] + 'con'
for t in libml.bits(model.type):
if t == libml.SVM:
helper.mkpath(os.path.join(args.output, "svm"))
con_path = os.path.join(path, "svm", con)
if t == libml.LIN:
helper.mkpath(os.path.join(args.output, "lin"))
con_path = os.path.join(path, "lin", con)
if t == libml.CRF:
helper.mkpath(os.path.join(args.output, "crf"))
con_path = os.path.join(path, "crf", con)
write_con(con_path, data, labels[t])
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-i",
dest="input",
help="The input files to predict",
)
parser.add_argument(
"-o",
dest="output",
help="The directory to write the output",
)
parser.add_argument(
"-m",
dest="model",
help="The model to use for prediction",
)
parser.add_argument(
"-f",
dest="format",
help="Data format ( " + ' | '.join(Note.supportedFormats()) + " )",
)
parser.add_argument("-crf",
dest="with_crf",
help="Specify where to find crfsuite",
default=None)
parser.add_argument(
"-discontiguous_spans",
dest="third",
help="A flag indicating whether to have third/clustering pass",
action="store_true")
parser.add_argument(
"-umls_disambiguation",
dest="disambiguate",
help=
"A flag indicating whether to disambiguate CUI ID for identified entities in semeval",
action="store_true")
args = parser.parse_args()
# Error check: Ensure that file paths are specified
if not args.input:
print >> sys.stderr, '\n\tError: Must provide text files\n'
exit(1)
if not args.output:
print >> sys.stderr, '\n\tError: Must provide output directory\n'
exit(1)
if not args.model:
print >> sys.stderr, '\n\tError: Must provide path to model\n'
exit(1)
if not os.path.exists(args.model):
print >> sys.stderr, '\n\tError: Model does not exist: %s\n' % args.model
exit(1)
# Parse arguments
files = glob.glob(args.input)
helper.mkpath(args.output)
third = args.third
if args.format:
format = args.format
else:
print '\n\tERROR: must provide "format" argument\n'
exit()
if third is True and args.format == "i2b2":
exit("i2b2 formatting does not support disjoint spans")
# Tell user if not predicting
if not files:
print >> sys.stderr, "\n\tNote: You did not supply any input files\n"
exit()
# Predict
predict(files,
args.model,
args.output,
format=format,
third=third,
disambiguate=args.disambiguate)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-i",
dest="input",
help="The input files to predict",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/test_data/*"),
)
parser.add_argument(
"-o",
dest="output",
help="The directory to write the output",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/test_predictions"),
)
parser.add_argument(
"-m",
dest="model",
help="The model to use for prediction",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)), "../models/awesome.model"),
)
parser.add_argument("--no-svm", dest="no_svm", action="store_true", help="Disable SVM model generation")
parser.add_argument("--no-lin", dest="no_lin", action="store_true", help="Disable LIN model generation")
parser.add_argument("--no-crf", dest="no_crf", action="store_true", help="Disable CRF model generation")
args = parser.parse_args()
# Locate the test files
files = glob.glob(args.input)
# Load a model and make a prediction for each file
path = args.output
helper.mkpath(args.output)
model = Model.load(args.model)
if args.no_svm:
model.type &= ~libml.SVM
if args.no_lin:
model.type &= ~libml.LIN
if args.no_crf:
model.type &= ~libml.CRF
for txt in files:
data = read_txt(txt)
labels = model.predict(data)
con = os.path.split(txt)[-1]
con = con[:-3] + "con"
for t in libml.bits(model.type):
if t == libml.SVM:
helper.mkpath(os.path.join(args.output, "svm"))
con_path = os.path.join(path, "svm", con)
if t == libml.LIN:
helper.mkpath(os.path.join(args.output, "lin"))
con_path = os.path.join(path, "lin", con)
if t == libml.CRF:
helper.mkpath(os.path.join(args.output, "crf"))
con_path = os.path.join(path, "crf", con)
write_con(con_path, data, labels[t])
