def inner(
config: Dict[Text, Any] = None,
known_patterns: Optional[List[Dict[Text, Any]]] = None,
) -> RegexFeaturizer:
config = config or {}
return RegexFeaturizer(
{**RegexFeaturizer.get_default_config(), **config},
default_model_storage,
resource,
default_execution_context,
known_patterns,
)
def test_persist_load_for_finetuning(
create_featurizer: Callable[..., RegexFeaturizer],
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
resource: Resource,
whitespace_tokenizer: WhitespaceTokenizer,
):
patterns = [
{"pattern": "[0-9]+", "name": "number", "usage": "intent"},
{"pattern": "\\bhey*", "name": "hello", "usage": "intent"},
{"pattern": "[0-1]+", "name": "binary", "usage": "intent"},
]
featurizer = create_featurizer()
sentence = "hey how are you today 19.12.2019 ?"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
training_data = TrainingData([message], regex_features=patterns)
whitespace_tokenizer.process_training_data(training_data)
featurizer.train(training_data)
loaded_featurizer = RegexFeaturizer.load(
RegexFeaturizer.get_default_config(),
default_model_storage,
resource,
dataclasses.replace(default_execution_context, is_finetuning=True),
)
# Test component loaded in finetune mode and also with
# same patterns as before and vocabulary statistics
assert loaded_featurizer.known_patterns == featurizer.known_patterns
assert loaded_featurizer.finetune_mode
new_lookups = [{"name": "plates", "elements": "data/test/lookup_tables/plates.txt"}]
training_data = TrainingData()
training_data.lookup_tables = new_lookups
loaded_featurizer.train(training_data)
# Test merging of a new pattern to an already trained component.
assert len(loaded_featurizer.known_patterns) == 4
def test_vocabulary_expand_for_finetuning(
create_featurizer: Callable[..., RegexFeaturizer],
default_model_storage: ModelStorage,
resource: Resource,
default_execution_context: ExecutionContext,
whitespace_tokenizer: WhitespaceTokenizer,
):
patterns = [
{"pattern": "[0-9]+", "name": "number", "usage": "intent"},
{"pattern": "\\bhey*", "name": "hello", "usage": "intent"},
]
featurizer = create_featurizer()
sentence = "hey hey 2020"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
training_data = TrainingData([message], regex_features=patterns)
whitespace_tokenizer.process_training_data(training_data)
featurizer.train(training_data)
featurizer.process_training_data(training_data)
# Test featurization of message
expected = np.array([1, 0])
expected_cls = np.array([1, 1])
seq_vecs, sen_vec = message.get_sparse_features(TEXT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vec:
sen_vec = sen_vec.features
assert (3, 2) == seq_vecs.shape
assert (1, 2) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
loaded_featurizer = RegexFeaturizer.load(
RegexFeaturizer.get_default_config(),
default_model_storage,
resource,
dataclasses.replace(default_execution_context, is_finetuning=True),
)
new_patterns = [
{"pattern": "\\btoday*", "name": "day", "usage": "intent"},
{"pattern": "\\bhey+", "name": "hello", "usage": "intent"},
]
new_sentence = "hey today"
message = Message(data={TEXT: new_sentence})
message.set(RESPONSE, new_sentence)
message.set(INTENT, "intent")
new_training_data = TrainingData([message], regex_features=patterns + new_patterns)
whitespace_tokenizer.process_training_data(new_training_data)
loaded_featurizer.train(new_training_data)
loaded_featurizer.process_training_data(new_training_data)
# Test featurization of message, this time for the extra pattern as well.
expected_token_1 = np.array([1, 0, 0])
expected_token_2 = np.array([0, 0, 1])
expected_cls = np.array([1, 0, 1])
seq_vecs, sen_vec = message.get_sparse_features(TEXT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vec:
sen_vec = sen_vec.features
assert (2, 3) == seq_vecs.shape
assert (1, 3) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected_token_1)
assert np.all(seq_vecs.toarray()[1] == expected_token_2)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
# let's check if the order of patterns is preserved
for old_index, pattern in enumerate(featurizer.known_patterns):
assert pattern["name"] == loaded_featurizer.known_patterns[old_index]["name"]
# we also modified a pattern, check if that is correctly modified
pattern_to_check = [
pattern
for pattern in loaded_featurizer.known_patterns
if pattern["name"] == "hello"
]
assert pattern_to_check == [new_patterns[1]]
