def objective_function_parsing(feature_weights):
"""
The objective function to optimize for dependency parsing.
:param feature_weights: a numpy array; these are the weights of the features
that we want to learn
:return: the error that should be minimized
"""
train_subset, train_labels_subset = task_utils.get_data_subsets(
feature_values, feature_weights, X_train, y_train, PARSING,
TASK2TRAIN_EXAMPLES[PARSING])
val_accuracy, _ = task_utils.train_and_evaluate_parsing(
train_subset, train_labels_subset, X_val, y_val,
parser_output_path=parser_output_path,
perl_script_path=perl_script_path)
error = 100 - float(val_accuracy)
return error
def objective_function_pos(feature_weights):
"""
The objective function to optimize for POS tagging.
:param feature_weights: a numpy array; these are the weights of the features
that we want to learn
:return: the error that should be minimized
"""
train_subset, train_labels_subset = task_utils.get_data_subsets(
feature_values, feature_weights, X_train, y_train, POS,
TASK2TRAIN_EXAMPLES[POS])
# train and evaluate the tagger; we input the test documents here but only
# minimize the validation error
val_accuracy, _ = task_utils.train_and_evaluate_pos(
train_subset, train_labels_subset, X_val, y_val)
# we minimize the error; the lower the better
error = 1 - float(val_accuracy)
return error
def objective_function_slot_filling(feature_weights):
train_subset, train_labels_subset = task_utils.get_data_subsets(
feature_values, feature_weights, X_train, y_train, SLOT_FILLING,
TASK2TRAIN_EXAMPLES[SLOT_FILLING])
print("Running evaluation for slot filling")
# train and evaluate the tagger; we input the test documents here but only
# minimize the validation error
#print("Training samples {} {}".format(X_train[:10], y_train[:10]))
#data_utils.dump_to_conll(train_subset, train_labels_subset, "test_dump.txt")
dev_f1_score, test_f1_score = task_utils.train_and_evaluate_slot_filling_MTL(
train_subset, train_labels_subset, X_val, y_val, args=args)
# we minimize the error; the lower the better
error = 1 - dev_f1_score
return error
most_similar_domain = similarity.get_most_similar_domain(
trg_domain, domain2term_dist)
train_subset, labels_subset, _ = domain2train_data[
most_similar_domain]
train_subset, _, labels_subset, _ = train_test_split(
train_subset,
labels_subset,
train_size=num_train_examples,
stratify=labels_subset
if args.task == SENTIMENT else None)
elif baseline == MOST_SIMILAR_EXAMPLES:
print('Selecting the most similar examples...')
one_all_weights = np.ones(len(feature_names))
one_all_weights[1:] = 0
train_subset, labels_subset = task_utils.get_data_subsets(
feature_values, one_all_weights, X_train, y_train,
args.task, num_train_examples)
else:
raise ValueError('%s is not a baseline.' % baseline)
# train the baseline
val_accuracy, test_accuracy = train_and_evaluate(
train_subset,
labels_subset,
X_val,
y_val,
X_test,
y_test,
parser_output_path=parser_output_path,
perl_script_path=perl_script_path)
run_dict[baseline].append((val_accuracy, test_accuracy))
