def test_models(file_in, file_out, model_files, source_attr, target_attr,
oov_test_file, oov_part, pos_attr, test_indiv):
"""\
Test all the given models on the selected file and save the target.
If oov_test_file is set, performs also OOV evaluation.
If test_pos is True, prints detailed results for various POSs.
"""
# load testing data
log_info('Loading data: ' + file_in)
data = DataSet()
data.load_from_arff(file_in)
forms = data[source_attr]
# apply all models
for model_num, model_file in enumerate(model_files, start=1):
model = Model.load_from_file(model_file)
log_info('Applying model: ' + model_file)
rules = model.classify(data)
output_attr = 'OUTPUT_M' + str(model_num)
data.add_attrib(Attribute(output_attr, 'string'), rules)
if test_indiv:
good = count_correct(data, model.class_attr, output_attr)
print_score(good, len(data), 'Model accuracy')
forms = [inflect(form, rule) for form, rule in zip(forms, rules)]
forms_attr = 'FORMS_M' + str(model_num)
data.add_attrib(Attribute(forms_attr, 'string'), forms)
# test the final performance
log_info('Evaluating...')
good = count_correct(data, target_attr, forms_attr)
print_score(good, len(data), 'ALL')
# evaluate without punctuation
evaluate_nopunct(data, source_attr, target_attr, forms_attr)
# evaluate forms different from lemma
evaluate_nolemma(data, source_attr, target_attr, forms_attr)
# load training data for OOV tests, evaluate on OOV
if oov_test_file:
evaluate_oov(data, source_attr, target_attr, forms_attr,
oov_test_file, oov_part)
# test on different POSes
if pos_attr:
evaluate_poses(data, target_attr, forms_attr, pos_attr)
# save the classification results
log_info('Saving data: ' + file_out)
data.save_to_arff(file_out)
def test_models(file_in, file_out, model_files, source_attr, target_attr,
oov_test_file, oov_part, pos_attr, test_indiv):
"""\
Test all the given models on the selected file and save the target.
If oov_test_file is set, performs also OOV evaluation.
If test_pos is True, prints detailed results for various POSs.
"""
# load testing data
log_info('Loading data: ' + file_in)
data = DataSet()
data.load_from_arff(file_in)
forms = data[source_attr]
# apply all models
for model_num, model_file in enumerate(model_files, start=1):
model = Model.load_from_file(model_file)
log_info('Applying model: ' + model_file)
rules = model.classify(data)
output_attr = 'OUTPUT_M' + str(model_num)
data.add_attrib(Attribute(output_attr, 'string'), rules)
if test_indiv:
good = count_correct(data, model.class_attr, output_attr)
print_score(good, len(data), 'Model accuracy')
forms = [inflect(form, rule) for form, rule in zip(forms, rules)]
forms_attr = 'FORMS_M' + str(model_num)
data.add_attrib(Attribute(forms_attr, 'string'), forms)
# test the final performance
log_info('Evaluating...')
good = count_correct(data, target_attr, forms_attr)
print_score(good, len(data), 'ALL')
# evaluate without punctuation
evaluate_nopunct(data, source_attr, target_attr, forms_attr)
# evaluate forms different from lemma
evaluate_nolemma(data, source_attr, target_attr, forms_attr)
# load training data for OOV tests, evaluate on OOV
if oov_test_file:
evaluate_oov(data, source_attr, target_attr, forms_attr, oov_test_file,
oov_part)
# test on different POSes
if pos_attr:
evaluate_poses(data, target_attr, forms_attr, pos_attr)
# save the classification results
log_info('Saving data: ' + file_out)
data.save_to_arff(file_out)
def main():
"""\
Main application entry: parse command line and run the test.
"""
opts, filenames = getopt.getopt(sys.argv[1:], 'g:p:ai')
show_help = False
annot_errors = False
gold = None
predicted = 'PREDICTED'
ignore_case = False
for opt, arg in opts:
if opt == '-g':
gold = arg
elif opt == '-p':
predicted = arg
elif opt == '-a':
annot_errors = True
elif opt == '-i':
ignore_case = True
# display help and exit
if len(filenames) != 2 or not gold or show_help:
display_usage()
sys.exit(1)
# run the training
filename_in, filename_out = filenames
data = DataSet()
log_info('Loading data: ' + filename_in)
data.load_from_arff(filename_in)
if ignore_case:
cmp_func = lambda a, b: a.lower() != b.lower()
else:
cmp_func = lambda a, b: a != b
if annot_errors:
log_info('Annotating errors...')
err_ind = [
'ERR' if cmp_func(i[gold], i[predicted]) else '' for i in data
]
data.add_attrib(Attribute('ERROR_IND', 'string'), err_ind)
else:
log_info('Selecting errors...')
data = data[lambda _, i: cmp_func(i[gold], i[predicted])]
log_info('Saving data: ' + filename_out)
data.save_to_arff(filename_out)
def main():
"""\
Main application entry: parse command line and run the test.
"""
opts, filenames = getopt.getopt(sys.argv[1:], "g:p:ai")
show_help = False
annot_errors = False
gold = None
predicted = "PREDICTED"
ignore_case = False
for opt, arg in opts:
if opt == "-g":
gold = arg
elif opt == "-p":
predicted = arg
elif opt == "-a":
annot_errors = True
elif opt == "-i":
ignore_case = True
# display help and exit
if len(filenames) != 2 or not gold or show_help:
display_usage()
sys.exit(1)
# run the training
filename_in, filename_out = filenames
data = DataSet()
log_info("Loading data: " + filename_in)
data.load_from_arff(filename_in)
if ignore_case:
cmp_func = lambda a, b: a.lower() != b.lower()
else:
cmp_func = lambda a, b: a != b
if annot_errors:
log_info("Annotating errors...")
err_ind = ["ERR" if cmp_func(i[gold], i[predicted]) else "" for i in data]
data.add_attrib(Attribute("ERROR_IND", "string"), err_ind)
else:
log_info("Selecting errors...")
data = data[lambda _, i: cmp_func(i[gold], i[predicted])]
log_info("Saving data: " + filename_out)
data.save_to_arff(filename_out)
