def test(model, testset, outfile):
sys.stderr.write("Testing.\n")
testing_tagfile = tempfile.NamedTemporaryFile(delete=False)
testing_features = tempfile.NamedTemporaryFile(delete=False)
# tag the testing file with placeholder tags
crf_formatter.text_to_tagged(testset, testing_tagfile.name)
# generate features for the tagged file
features.generate(testing_tagfile.name, testing_features.name)
# Generate predicted tags using the trained model
sys.stderr.write("Predicting tags.\n")
# Call CRFSuite using the command line to tag the testing_features
# using your trained CRF model.
cmd = ["crfsuite", "tag", "-m", model, testing_features.name]
predictions, _ = Popen(cmd, stdout=PIPE).communicate()
# pair the testing characters with their predicted tags
# convert the tagged file back to lines of text
crf_formatter.predictions_to_text(testing_tagfile.name,
predictions.split('\n'),
outfile=outfile)
# clear the temp files
os.unlink(testing_tagfile.name)
os.unlink(testing_features.name)
def test(model, testset, outfile):
sys.stderr.write("Testing.\n")
testing_tagfile = tempfile.NamedTemporaryFile(delete=False)
testing_features = tempfile.NamedTemporaryFile(delete=False)
# tag the testing file with placeholder tags
crf_formatter.text_to_tagged(testset, testing_tagfile.name)
# generate features for the tagged file
features.generate(testing_tagfile.name, testing_features.name)
# Generate predicted tags using the trained model
sys.stderr.write("Predicting tags.\n")
# Call CRFSuite using the command line to tag the testing_features
# using your trained CRF model.
cmd = None ### YOUR CODE HERE: define the command.
predictions, _ = Popen(cmd, stdout=PIPE).communicate()
# pair the testing characters with their predicted tags
# convert the tagged file back to lines of text
crf_formatter.predictions_to_text(testing_tagfile.name, predictions.split('\n'), outfile=outfile)
# clear the temp files
os.unlink(testing_tagfile.name)
os.unlink(testing_features.name)
def train(model, trainset):
sys.stderr.write("Training.\n")
training_tagfile = tempfile.NamedTemporaryFile(delete=False)
training_features = tempfile.NamedTemporaryFile(delete=False)
# tag the segmented training set
crf_formatter.text_to_tagged(trainset, training_tagfile.name)
# generate features for the tagged set
features.generate(training_tagfile.name, training_features.name)
# Call CRFSuite using the command line to train a CRF model.
cmd = ["crfsuite", "learn", "-m", model, training_features.name]
call(cmd)
# clear the temp files
os.unlink(training_tagfile.name)
os.unlink(training_features.name)
def train(model, trainset):
sys.stderr.write("Training.\n")
training_tagfile = tempfile.NamedTemporaryFile(delete=False)
training_features = tempfile.NamedTemporaryFile(delete=False)
# tag the segmented training set
crf_formatter.text_to_tagged(trainset, training_tagfile.name)
# generate features for the tagged set
features.generate(training_tagfile.name, training_features.name)
# Call CRFSuite using the command line to train a CRF model.
cmd = ["crfsuite", "learn", "-m", model, training_features.name]
call(cmd)
# clear the temp files
os.unlink(training_tagfile.name)
os.unlink(training_features.name)
