def task(name):
if name == 'offenseval':
return of.Offenseval()
elif name == 'vua-format':
return vf.VuaFormat()
else:
raise ValueError("task name is unknown. You can add a custom task in 'tasks'")
def test_grid_search():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=True, proportion_train=0.1, proportion_dev=0.01)
params = {'clf__C': (0.1, 1)}
best_sys_y = utils.grid_search(pipelines.svm_libsvc_counts(), params,
train_X, train_y, test_X)
assert len(best_sys_y) == len(test_y)
def test_naive_bayes_pipeline():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=True, proportion_train=0.1, proportion_dev=0.01)
pipe = pipelines.naive_bayes()
pipe.fit(train_X, train_y)
sys_y = pipe.predict(test_X)
assert len(sys_y) == len(test_y)
def encode_data(data_dir):
print('Loading data...')
task = of.Offenseval()
task.load(data_dir=data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(task, split_train_dev=False)
print(len(train_X), 'train sequences')
print(len(test_X), 'test sequences')
train_X, train_y, test_X, test_y = encode(train_X, train_y, test_X, test_y)
return train_X, train_y, test_X, test_y
def test_offenseval_data_extraction_task_a():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y = task.train_instances()
test_X, test_y = task.test_instances()
assert len(train_X) == 13240
assert len(test_y) == 320
assert isinstance(train_X[0], str)
labels = set(test_y)
assert len(labels) == 2
def test_data_split_on_offenseval():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=False)
assert len(train_X) == 13240
assert len(test_X) == 320
assert isinstance(train_X[0], str)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=True)
assert len(train_X) == 13240 * 0.9
assert len(test_X) == 13240 * 0.1
def test_representation():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=True, proportion_train=0.1, proportion_dev=0.01)
prep = preprocessing.Preprocessor(tokenize=True,
normalize_tweet=False,
lowercase=False,
lemmatize=False)
train_X = prep.transform(train_X)
frmt = representation.count_vectorizer()
train_X = frmt.fit_transform(train_X, train_y)
assert not isinstance(train_X[0], str)
def test_preprocessors():
task = of.Offenseval()
task.load(offenseval_data_dir)
train_X, train_y, test_X, test_y = utils.get_instances(
task, split_train_dev=True, proportion_train=0.1, proportion_dev=0.01)
prep = preprocessing.Preprocessor(tokenize=False,
normalize_tweet=False,
lowercase=False,
lemmatize=False)
train_X_prep = prep.transform(train_X)
assert len(train_X_prep) == len(train_X)
assert isinstance(train_X_prep[0], str)
prep = preprocessing.Preprocessor(tokenize=True,
normalize_tweet=True,
lowercase=True,
lemmatize=True)
train_X_prep = prep.transform(train_X)
assert len(train_X_prep) == len(train_X)
assert isinstance(train_X_prep[0], str)