def command_out_of_vocabulary(_args):
friend_pairs = util.read_words(_args.friends_file_name)
model_es, model_pt = util.read_models(_args)
words_es = (friend_pair.word_es for friend_pair in friend_pairs)
words_pt = (friend_pair.word_pt for friend_pair in friend_pairs)
print("OOV es:")
for word_es in word_vectors.words_out_of_vocabulary(model_es, words_es):
print(word_es)
print('')
print("OOV pt:")
for word_pt in word_vectors.words_out_of_vocabulary(model_pt, words_pt):
print(word_pt)
def command_out_of_vocabulary(args_):
friend_pairs = util.read_words(args_.friends_file_name)
model_es, model_pt = util.read_models(args_)
words_es = (friend_pair.word_es for friend_pair in friend_pairs)
words_pt = (friend_pair.word_pt for friend_pair in friend_pairs)
print("OOV es:")
for word_es in word_vectors.words_out_of_vocabulary(
model_es, words_es):
print(word_es)
print('')
print("OOV pt:")
for word_pt in word_vectors.words_out_of_vocabulary(
model_pt, words_pt):
print(word_pt)
def command_classify(_args):
training_friend_pairs = util.read_words(_args.training_friends_file_name)
testing_friend_pairs = util.read_words(_args.testing_friends_file_name)
model_es, model_pt = util.read_models(_args)
T = linear_trans.load_linear_transformation(_args.translation_matrix_file_name)
clf = CLF_OPTIONS[_args.classifier]
if _args.cross_validation:
X, y, _ = classifier.features_labels_and_scaler(training_friend_pairs + testing_friend_pairs, model_es,
model_pt, T, backwards=_args.backwards)
# FIXME: I think it should scale on each different training set.
measures = classifier.classify_with_cross_validation(X, y, clf=clf)
print('')
print("Cross-validation measures with 95% of confidence:")
for measure_name, (mean, delta) in measures.items():
print("{measure_name}: {mean:0.4f} ± {delta:0.4f} --- [{inf:0.4f}, {sup:0.4f}]".format(
measure_name=measure_name, mean=mean, delta=delta, inf=mean - delta, sup=mean + delta))
print('')
mean_measures = {measure_name: mean for measure_name, (mean, delta) in measures.items()}
__print_metrics_matrix(mean_measures)
__print_confusion_matrix(mean_measures)
else:
X_train, y_train, scaler = classifier.features_labels_and_scaler(training_friend_pairs, model_es, model_pt,
T, backwards=_args.backwards)
X_test, y_test, _ = classifier.features_labels_and_scaler(testing_friend_pairs, model_es, model_pt, T,
scaler=scaler, backwards=_args.backwards)
measures = classifier.classify(X_train, X_test, y_train, y_test)
print('')
__print_metrics_matrix(measures)
__print_confusion_matrix(measures)
def command_classify(args_):
training_friend_pairs = util.read_words(
args_.training_friends_file_name)
testing_friend_pairs = util.read_words(args_.testing_friends_file_name)
model_es, model_pt = util.read_models(args_)
T = linear_trans.load_linear_transformation(
args_.translation_matrix_file_name)
clf = classifier.build_classifier(CLF_OPTIONS[args_.classifier])
if args_.cross_validation:
friend_pairs = training_friend_pairs + testing_friend_pairs
X, y, = classifier.features_and_labels(
friend_pairs,
model_es,
model_pt,
T,
backwards=args_.backwards,
topx=args_.top,
use_taxonomy=args_.use_taxonomy)
measures = classifier.classify_with_cross_validation(X, y, clf=clf)
print('')
print("Cross-validation measures with 95% of confidence:")
for measure_name, (mean, delta) in measures.items():
print(
"{measure_name}: {mean:0.4f} ± {delta:0.4f} --- [{inf:0.4f}, {sup:0.4f}]"
.format(measure_name=measure_name,
mean=mean,
delta=delta,
inf=mean - delta,
sup=mean + delta))
print('')
mean_measures = {
measure_name: mean
for measure_name, (mean, delta) in measures.items()
}
_print_metrics_matrix(mean_measures)
_print_confusion_matrix(mean_measures)
else:
X_train, y_train = classifier.features_and_labels(
training_friend_pairs,
model_es,
model_pt,
T,
backwards=args_.backwards,
topx=args_.top,
use_taxonomy=args_.use_taxonomy)
X_test, y_test = classifier.features_and_labels(
testing_friend_pairs,
model_es,
model_pt,
T,
backwards=args_.backwards,
topx=args_.top,
use_taxonomy=args_.use_taxonomy)
measures = classifier.classify(X_train, X_test, y_train, y_test,
clf)
print('')
_print_metrics_matrix(measures)
_print_confusion_matrix(measures)
