def test_make_bert_embeddings(self):
model = get_bert_model()
device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
train_dataset = get_mnli_test_dataset("train")
train_dataset = list(train_dataset[TEXT_COL])
training_embeddings = make_bert_embeddings(train_dataset, model, device)
assert training_embeddings is not None
def test_explain_model_BERT_seq_classification(self):
device = torch.device(
"cpu" if not torch.cuda.is_available() else "cuda")
mnli_test_dataset = get_mnli_test_dataset()
text = "rare bird has more than enough charm to make it memorable."
model = get_bert_model()
model.to(device)
interpreter_unified = UnifiedInformationExplainer(
model=model,
train_dataset=list(mnli_test_dataset),
device=device,
target_layer=14,
)
explanation_unified = interpreter_unified.explain_local(text)
valid_imp_vals = np.array([
0.16004231572151184, 0.17308972775936127, 0.18205846846103668,
0.26146841049194336, 0.25957807898521423, 0.3549807369709015,
0.23873654007911682, 0.2826242744922638, 0.2700383961200714,
0.3673151433467865, 0.3899800479412079, 0.20173774659633636
])
print(explanation_unified.local_importance_values)
local_importance_values = np.array(
explanation_unified.local_importance_values)
cos_sim = dot(valid_imp_vals, local_importance_values) / (
norm(valid_imp_vals) * norm(local_importance_values))
assert cos_sim >= 0.80
def test_explain_model_local(self):
"""
Test for explain_local of classical explainer
:return:
"""
train_df = get_mnli_test_dataset('train')
X_str = train_df['sentence1']
ylabels = train_df['genre']
X_train, X_test, y_train, y_test = train_test_split(X_str,
ylabels,
train_size=0.8,
test_size=0.2)
label_encoder = LabelEncoder()
y_train = label_encoder.fit_transform(y_train)
explainer = ClassicalTextExplainer()
classifier, best_params = explainer.fit(X_train, y_train)
explainer.preprocessor.labelEncoder = label_encoder
local_explanantion = explainer.explain_local(DOCUMENT)
assert len(local_explanantion.local_importance_values) == len(
local_explanantion.features)
class TestMSRAUtils(object):
train_dataset = get_mnli_test_dataset()
def test_working(self):
assert True
def test_get_single_embedding(self):
model = get_bert_model()
device = torch.device(
"cpu" if not torch.cuda.is_available() else "cuda")
text = "rare bird has more than enough charm to make it memorable."
embedded_input = get_single_embedding(model, text, device)
assert embedded_input is not None
def test_make_bert_embeddings(self):
model = get_bert_model()
device = torch.device(
"cpu" if not torch.cuda.is_available() else "cuda")
train_dataset = get_mnli_test_dataset()
training_embeddings = make_bert_embeddings(train_dataset, model,
device)
assert training_embeddings is not None