def test_count_vector_featurizer_shared_vocab(sentence, intent, response,
text_features, intent_features,
response_features):
ftr = CountVectorsFeaturizer({
"token_pattern": r"(?u)\b\w+\b",
"use_shared_vocab": True
})
tk = WhitespaceTokenizer()
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
data = TrainingData([train_message])
tk.train(data)
ftr.train(data)
assert np.all(
train_message.get(SPARSE_FEATURE_NAMES[TEXT]).toarray()[0] ==
text_features)
assert np.all(
train_message.get(SPARSE_FEATURE_NAMES[INTENT]).toarray()[0] ==
intent_features)
assert np.all(
train_message.get(SPARSE_FEATURE_NAMES[RESPONSE]).toarray()[0] ==
response_features)
def test_convert_featurizer_process(monkeypatch: MonkeyPatch):
tokenizer = WhitespaceTokenizer()
monkeypatch.setattr(ConveRTFeaturizer, "_get_validated_model_url",
lambda x: RESTRICTED_ACCESS_URL)
component_config = {
"name": "ConveRTFeaturizer",
"model_url": RESTRICTED_ACCESS_URL
}
featurizer = ConveRTFeaturizer(component_config)
sentence = "Hey how are you today ?"
message = Message.build(text=sentence)
td = TrainingData([message])
tokenizer.train(td)
tokens = featurizer.tokenize(message, attribute=TEXT)
featurizer.process(message, tf_hub_module=featurizer.module)
expected = np.array(
[2.2636216, -0.26475656, -1.1358104, -0.49751878, -1.3946456])
expected_cls = np.array(
[1.0251294, -0.04053932, -0.7018805, -0.82054937, -0.75054353])
seq_vecs, sent_vecs = message.get_dense_features(TEXT, [])
seq_vecs = seq_vecs.features
sent_vecs = sent_vecs.features
assert len(tokens) == len(seq_vecs)
assert np.allclose(seq_vecs[0][:5], expected, atol=1e-5)
assert np.allclose(sent_vecs[-1][:5], expected_cls, atol=1e-5)
def test_cvf_incremental_train_vocabulary_overflow(tmp_path: Path, ):
additional_size = 3
original_train_text = "hello my name is John."
additional_train_text = "I am also new."
tokenizer = WhitespaceTokenizer()
original_featurizer = CountVectorsFeaturizer(
{"additional_vocabulary_size": {
"text": additional_size
}},
finetune_mode=False,
)
train_message = Message(data={"text": original_train_text})
data = TrainingData([train_message])
tokenizer.train(data)
original_featurizer.train(data)
file_dict = original_featurizer.persist("ftr", str(tmp_path))
# load original_featurizer
meta = original_featurizer.component_config.copy()
meta.update(file_dict)
new_featurizer = CountVectorsFeaturizer.load(meta,
str(tmp_path),
should_finetune=True)
additional_train_message = Message(data={"text": additional_train_text})
data = TrainingData([train_message, additional_train_message])
tokenizer.train(data)
with pytest.warns(UserWarning) as warning:
new_featurizer.train(data)
assert "New data contains vocabulary of size" in warning[0].message.args[0]
def test_count_vector_featurizer_response_attribute_featurization(
sentence, intent, response, intent_features, response_features):
ftr = CountVectorsFeaturizer({"token_pattern": r"(?u)\b\w+\b"})
tk = WhitespaceTokenizer()
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
# add a second example that has some response, so that the vocabulary for
# response exists
second_message = Message("hello")
second_message.set(RESPONSE, "hi")
second_message.set(INTENT, "greet")
data = TrainingData([train_message, second_message])
tk.train(data)
ftr.train(data)
if intent_features:
assert (train_message.get(
SPARSE_FEATURE_NAMES[INTENT]).toarray()[0] == intent_features)
else:
assert train_message.get(SPARSE_FEATURE_NAMES[INTENT]) is None
if response_features:
assert (train_message.get(
SPARSE_FEATURE_NAMES[RESPONSE]).toarray()[0] == response_features)
else:
assert train_message.get(SPARSE_FEATURE_NAMES[RESPONSE]) is None
def test_count_vector_featurizer_attribute_featurization(
sentence, intent, response, intent_features, response_features):
ftr = CountVectorsFeaturizer({"token_pattern": r"(?u)\b\w+\b"})
tk = WhitespaceTokenizer()
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
data = TrainingData([train_message])
tk.train(data)
ftr.train(data)
if intent_features:
assert (train_message.get(
SPARSE_FEATURE_NAMES[INTENT]).toarray()[0] == intent_features)
else:
assert train_message.get(SPARSE_FEATURE_NAMES[INTENT]) is None
if response_features:
assert (train_message.get(
SPARSE_FEATURE_NAMES[RESPONSE]).toarray()[0] == response_features)
else:
assert train_message.get(SPARSE_FEATURE_NAMES[RESPONSE]) is None
def test_train_tokenizer(text: Text, expected_tokens: List[Text],
expected_indices: List[Tuple[int]]):
tk = WhitespaceTokenizer()
message = Message.build(text=text)
message.set(RESPONSE, text)
message.set(INTENT, text)
training_data = TrainingData()
training_data.training_examples = [message]
tk.train(training_data)
for attribute in [RESPONSE, TEXT]:
tokens = training_data.training_examples[0].get(
TOKENS_NAMES[attribute])
assert [t.text for t in tokens] == expected_tokens
assert [t.start for t in tokens] == [i[0] for i in expected_indices]
assert [t.end for t in tokens] == [i[1] for i in expected_indices]
# check intent attribute
tokens = training_data.training_examples[0].get(TOKENS_NAMES[INTENT])
assert [t.text for t in tokens] == [text]
def test_whitespace_training(supervised_embeddings_config):
examples = [
Message(
"Any Mexican restaurant will do",
{
"intent": "restaurant_search",
"entities": [
{"start": 4, "end": 11, "value": "Mexican", "entity": "cuisine"}
],
},
),
Message(
"I want Tacos!",
{
"intent": "restaurant_search",
"entities": [
{"start": 7, "end": 12, "value": "Mexican", "entity": "cuisine"}
],
},
),
]
tk = WhitespaceTokenizer()
tk.train(TrainingData(training_examples=examples), supervised_embeddings_config)
assert examples[0].data.get(TOKENS_NAMES[TEXT])[0].text == "Any"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[1].text == "Mexican"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[2].text == "restaurant"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[3].text == "will"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[4].text == "do"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[0].text == "I"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[1].text == "want"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[2].text == "Tacos"
def test_count_vector_featurizer_attribute_featurization(
sentence, intent, response, intent_features, response_features):
ftr = CountVectorsFeaturizer()
tk = WhitespaceTokenizer()
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
data = TrainingData([train_message])
tk.train(data)
ftr.train(data)
intent_seq_vecs, intent_sen_vecs = train_message.get_sparse_features(
INTENT, [])
response_seq_vecs, response_sen_vecs = train_message.get_sparse_features(
RESPONSE, [])
if intent_features:
assert intent_seq_vecs.toarray()[0] == intent_features
assert intent_sen_vecs is None
else:
assert intent_seq_vecs is None
assert intent_sen_vecs is None
if response_features:
assert response_seq_vecs.toarray()[0] == response_features
assert response_sen_vecs is not None
else:
assert response_seq_vecs is None
assert response_sen_vecs is None
def test_count_vector_featurizer_shared_vocab(sentence, intent, response,
text_features, intent_features,
response_features):
ftr = CountVectorsFeaturizer({
"token_pattern": r"(?u)\b\w+\b",
"use_shared_vocab": True
})
tk = WhitespaceTokenizer()
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
data = TrainingData([train_message])
tk.train(data)
ftr.train(data)
seq_vec, sen_vec = train_message.get_sparse_features(TEXT, [])
assert np.all(seq_vec.toarray()[0] == text_features)
assert sen_vec is not None
seq_vec, sen_vec = train_message.get_sparse_features(INTENT, [])
assert np.all(seq_vec.toarray()[0] == intent_features)
assert sen_vec is None
seq_vec, sen_vec = train_message.get_sparse_features(RESPONSE, [])
assert np.all(seq_vec.toarray()[0] == response_features)
assert sen_vec is not None
async def test_train_persist_load_with_composite_entities(
crf_entity_extractor: Callable[[Dict[Text, Any]],
CRFEntityExtractorGraphComponent],
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
):
importer = RasaFileImporter(
training_data_paths=["data/test/demo-rasa-composite-entities.yml"])
training_data = importer.get_nlu_data()
tokenizer = WhitespaceTokenizer()
tokenizer.train(training_data)
crf_extractor = crf_entity_extractor({})
crf_extractor.train(training_data)
message = Message(data={TEXT: "I am looking for an italian restaurant"})
tokenizer.process(message)
message2 = copy.deepcopy(message)
processed_message = crf_extractor.process([message])[0]
loaded_extractor = CRFEntityExtractorGraphComponent.load(
CRFEntityExtractorGraphComponent.get_default_config(),
default_model_storage,
Resource("CRFEntityExtractor"),
default_execution_context,
)
processed_message2 = loaded_extractor.process([message2])[0]
assert processed_message2.fingerprint() == processed_message.fingerprint()
def test_use_shared_vocab_exception(
initial_train_text: Text,
additional_train_text: Text,
use_shared_vocab: bool,
tmp_path: Path,
):
"""Tests if an exception is raised when `use_shared_vocab` is set to True
during incremental training."""
tk = WhitespaceTokenizer()
initial_cvf = CountVectorsFeaturizer(
component_config={"use_shared_vocab": use_shared_vocab}
)
train_message = Message(data={"text": initial_train_text})
data = TrainingData([train_message])
tk.train(data)
initial_cvf.train(data)
file_dict = initial_cvf.persist("ftr", tmp_path)
meta = initial_cvf.component_config.copy()
meta.update(file_dict)
new_cvf = CountVectorsFeaturizer.load(meta, tmp_path, should_finetune=True)
additional_train_message = Message(data={"text": additional_train_text})
data = TrainingData([train_message, additional_train_message])
tk.train(data)
if use_shared_vocab:
with pytest.raises(Exception) as exec_info:
new_cvf.train(data)
assert (
"Using a shared vocabulary in `CountVectorsFeaturizer` is not supported"
in str(exec_info.value)
)
else:
new_cvf.train(data)
def test_flexible_nlu_pipeline():
message = Message("This is a test message.", data={"intent": "test"})
training_data = TrainingData([message, message, message, message, message])
tokenizer = WhitespaceTokenizer()
tokenizer.train(training_data)
featurizer = CountVectorsFeaturizer(
component_config={FEATURIZER_CLASS_ALIAS: "cvf_word"})
featurizer.train(training_data)
featurizer = CountVectorsFeaturizer(
component_config={
FEATURIZER_CLASS_ALIAS: "cvf_char",
"min_ngram": 1,
"max_ngram": 3,
"analyzer": "char_wb",
})
featurizer.train(training_data)
featurizer = LexicalSyntacticFeaturizer({})
featurizer.train(training_data)
assert len(message.features) == 6
assert message.features[0].origin == "cvf_word"
assert message.features[0].type == FEATURE_TYPE_SEQUENCE
assert message.features[1].origin == "cvf_word"
assert message.features[1].type == FEATURE_TYPE_SENTENCE
# cvf word is also extracted for the intent
assert message.features[2].origin == "cvf_word"
assert message.features[2].type == FEATURE_TYPE_SEQUENCE
assert message.features[3].origin == "cvf_char"
assert message.features[3].type == FEATURE_TYPE_SEQUENCE
assert message.features[4].origin == "cvf_char"
assert message.features[4].type == FEATURE_TYPE_SENTENCE
assert message.features[5].origin == "LexicalSyntacticFeaturizer"
assert message.features[5].type == FEATURE_TYPE_SEQUENCE
sequence_feature_dim = (message.features[0].features.shape[1] +
message.features[5].features.shape[1])
sentence_feature_dim = message.features[0].features.shape[1]
classifier = DIETClassifier(component_config={
FEATURIZERS: ["cvf_word", "LexicalSyntacticFeaturizer"]
})
model_data = classifier.preprocess_train_data(training_data)
assert len(model_data.get(TEXT_SENTENCE_FEATURES)) == 1
assert len(model_data.get(TEXT_SEQUENCE_FEATURES)) == 1
assert len(model_data.get(LABEL_SEQUENCE_FEATURES)) == 1
assert len(model_data.get(LABEL_SENTENCE_FEATURES)) == 0
assert model_data.get(TEXT_SEQUENCE_FEATURES)[0][0].shape == (
5,
sequence_feature_dim,
)
assert model_data.get(TEXT_SENTENCE_FEATURES)[0][0].shape == (
1,
sentence_feature_dim,
)
assert model_data.get(LABEL_SEQUENCE_FEATURES)[0][0].shape == (1, 1)
def test_cvf_incremental_train_vocabulary(
additional_size: Optional[int],
original_train_text: Text,
additional_train_text: Text,
total_vocabulary_size: int,
remaining_buffer_size: int,
tmp_path: Path,
):
tokenizer = WhitespaceTokenizer()
original_featurizer = CountVectorsFeaturizer(
{"additional_vocabulary_size": {
"text": additional_size
}},
finetune_mode=False,
)
train_message = Message(data={"text": original_train_text})
data = TrainingData([train_message])
tokenizer.train(data)
original_featurizer.train(data)
# Check total vocabulary size with buffer slots before finetuning
original_vocabulary = original_featurizer.vectorizers["text"].vocabulary_
assert len(original_vocabulary) == total_vocabulary_size
file_dict = original_featurizer.persist("ftr", str(tmp_path))
# load original_featurizer
meta = original_featurizer.component_config.copy()
meta.update(file_dict)
new_featurizer = CountVectorsFeaturizer.load(meta,
str(tmp_path),
should_finetune=True)
# Check total vocabulary size with buffer slots before finetuning
assert len(new_featurizer.vectorizers["text"].vocabulary_
) == total_vocabulary_size
additional_train_message = Message(data={"text": additional_train_text})
data = TrainingData([train_message, additional_train_message])
tokenizer.train(data)
new_featurizer.train(data)
new_vocabulary = new_featurizer.vectorizers["text"].vocabulary_
# Check total vocabulary size with buffer slots after finetuning
assert len(new_vocabulary) == total_vocabulary_size
# Check remaining buffer slots after finetuning
assert (len(new_vocabulary) -
new_featurizer._get_starting_empty_index(new_vocabulary) ==
remaining_buffer_size)
# Check indices of original vocabulary haven't changed in the new vocabulary
for vocab_token, vocab_index in original_vocabulary.items():
if not vocab_token.startswith("buf_"):
assert vocab_token in new_vocabulary
assert new_vocabulary.get(vocab_token) == vocab_index
def test_count_vector_featurizer_action_attribute_featurization(
sentence: Text,
action_name: Text,
action_text: Text,
action_name_features: np.ndarray,
response_features: np.ndarray,
):
ftr = CountVectorsFeaturizer({
"token_pattern": r"(?u)\b\w+\b",
"additional_vocabulary_size": {
"text": 0,
"response": 0,
"action_text": 0
},
})
tk = WhitespaceTokenizer()
train_message = Message(data={TEXT: sentence})
# this is needed for a valid training example
train_message.set(ACTION_NAME, action_name)
train_message.set(ACTION_TEXT, action_text)
# add a second example that has some response, so that the vocabulary for
# response exists
second_message = Message(data={TEXT: "hello"})
second_message.set(ACTION_TEXT, "hi")
second_message.set(ACTION_NAME, "greet")
data = TrainingData([train_message, second_message])
tk.train(data)
ftr.train(data)
action_name_seq_vecs, action_name_sen_vecs = train_message.get_sparse_features(
ACTION_NAME, [])
if action_name_seq_vecs:
action_name_seq_vecs = action_name_seq_vecs.features
if action_name_sen_vecs:
action_name_sen_vecs = action_name_sen_vecs.features
response_seq_vecs, response_sen_vecs = train_message.get_sparse_features(
ACTION_TEXT, [])
if response_seq_vecs:
response_seq_vecs = response_seq_vecs.features
if response_sen_vecs:
response_sen_vecs = response_sen_vecs.features
if action_name_features:
assert action_name_seq_vecs.toarray()[0] == action_name_features
assert action_name_sen_vecs is None
else:
assert action_name_seq_vecs is None
assert action_name_sen_vecs is None
if response_features:
assert response_seq_vecs.toarray()[0] == response_features
assert response_sen_vecs is not None
else:
assert response_seq_vecs is None
assert response_sen_vecs is None
def test_custom_intent_symbol(text, expected_tokens):
component_config = {"intent_tokenization_flag": True, "intent_split_symbol": "+"}
tk = WhitespaceTokenizer(component_config)
message = Message(text)
message.set(INTENT, text)
tk.train(TrainingData([message]))
assert [t.text for t in message.get(TOKENS_NAMES[INTENT])] == expected_tokens
def train_texts(texts: List[Text], model_name: Text,
model_weights: Text) -> List[Message]:
config = create_pretrained_transformers_config(model_name, model_weights)
whitespace_tokenizer = WhitespaceTokenizer()
transformer = HFTransformersNLP(config)
messages = [Message.build(text=text) for text in texts]
td = TrainingData(messages)
whitespace_tokenizer.train(td)
transformer.train(td)
return messages
def test_model_data_signature_with_entities(messages: List[Message],
entity_expected: bool):
classifier = DIETClassifier({"BILOU_flag": False})
training_data = TrainingData(messages)
# create tokens for entity parsing inside DIET
tokenizer = WhitespaceTokenizer()
tokenizer.train(training_data)
model_data = classifier.preprocess_train_data(training_data)
entity_exists = "entities" in model_data.get_signature().keys()
assert entity_exists == entity_expected
def test_check_check_correct_entity_annotations(text: Text, warnings: int):
reader = MarkdownReader()
tokenizer = WhitespaceTokenizer()
training_data = reader.reads(text)
tokenizer.train(training_data)
with pytest.warns(UserWarning) as record:
EntityExtractor.check_correct_entity_annotations(training_data)
assert len(record) == warnings
assert all([excerpt in record[0].message.args[0]]
for excerpt in ["Misaligned entity annotation in sentence"])
def test_apply_bilou_schema():
tokenizer = WhitespaceTokenizer()
message_1 = Message("Germany is part of the European Union")
message_1.set(
ENTITIES,
[
{"start": 0, "end": 7, "value": "Germany", "entity": "location"},
{
"start": 23,
"end": 37,
"value": "European Union",
"entity": "organisation",
},
],
)
message_2 = Message("Berlin is the capital of Germany")
message_2.set(
ENTITIES,
[
{"start": 0, "end": 6, "value": "Berlin", "entity": "location"},
{"start": 25, "end": 32, "value": "Germany", "entity": "location"},
],
)
training_data = TrainingData([message_1, message_2])
tokenizer.train(training_data)
bilou_utils.apply_bilou_schema(training_data)
assert message_1.get(BILOU_ENTITIES) == [
"U-location",
"O",
"O",
"O",
"O",
"B-organisation",
"L-organisation",
"O",
]
assert message_2.get(BILOU_ENTITIES) == [
"U-location",
"O",
"O",
"O",
"O",
"U-location",
"O",
]
def test_count_vector_featurizer_response_attribute_featurization(
sentence, intent, response, intent_features, response_features):
ftr = CountVectorsFeaturizer(
{"additional_vocabulary_size": {
"text": 0,
"response": 0
}})
tk = WhitespaceTokenizer()
train_message = Message(data={TEXT: sentence})
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
# add a second example that has some response, so that the vocabulary for
# response exists
second_message = Message(data={TEXT: "hello"})
second_message.set(RESPONSE, "hi")
second_message.set(INTENT, "greet")
data = TrainingData([train_message, second_message])
tk.train(data)
ftr.train(data)
intent_seq_vecs, intent_sen_vecs = train_message.get_sparse_features(
INTENT, [])
if intent_seq_vecs:
intent_seq_vecs = intent_seq_vecs.features
if intent_sen_vecs:
intent_sen_vecs = intent_sen_vecs.features
response_seq_vecs, response_sen_vecs = train_message.get_sparse_features(
RESPONSE, [])
if response_seq_vecs:
response_seq_vecs = response_seq_vecs.features
if response_sen_vecs:
response_sen_vecs = response_sen_vecs.features
if intent_features:
assert intent_seq_vecs.toarray()[0] == intent_features
assert intent_sen_vecs is None
else:
assert intent_seq_vecs is None
assert intent_sen_vecs is None
if response_features:
assert response_seq_vecs.toarray()[0] == response_features
assert response_sen_vecs is not None
else:
assert response_seq_vecs is None
assert response_sen_vecs is None
def test_whitespace_training(supervised_embeddings_config: RasaNLUModelConfig):
examples = [
Message(
data={
TEXT: "Any Mexican restaurant will do",
"intent": "restaurant_search",
"entities": [
{"start": 4, "end": 11, "value": "Mexican", "entity": "cuisine"}
],
}
),
Message(
data={
TEXT: "I want Tacos!",
"intent": "restaurant_search",
"entities": [
{"start": 7, "end": 12, "value": "Mexican", "entity": "cuisine"}
],
}
),
Message(data={TEXT: "action_restart", "action_name": "action_restart"}),
Message(
data={
TEXT: "Where are you going?",
ACTION_NAME: "Where are you going?",
ACTION_TEXT: "Where are you going?",
}
),
]
component_config = {"case_sensitive": False, "intent_tokenization_flag": True}
tk = WhitespaceTokenizer(component_config)
tk.train(TrainingData(training_examples=examples), supervised_embeddings_config)
assert examples[0].data.get(TOKENS_NAMES[TEXT])[0].text == "Any"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[1].text == "Mexican"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[2].text == "restaurant"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[3].text == "will"
assert examples[0].data.get(TOKENS_NAMES[TEXT])[4].text == "do"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[0].text == "I"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[1].text == "want"
assert examples[1].data.get(TOKENS_NAMES[TEXT])[2].text == "Tacos"
assert examples[2].data.get(TOKENS_NAMES[ACTION_NAME])[0].text == "action"
assert examples[2].data.get(TOKENS_NAMES[ACTION_NAME])[1].text == "restart"
assert examples[2].data.get(TOKENS_NAMES[TEXT])[0].text == "action_restart"
assert examples[2].data.get(TOKENS_NAMES[ACTION_TEXT]) is None
assert examples[3].data.get(TOKENS_NAMES[ACTION_TEXT])[0].text == "Where"
assert examples[3].data.get(TOKENS_NAMES[ACTION_TEXT])[1].text == "are"
assert examples[3].data.get(TOKENS_NAMES[ACTION_TEXT])[2].text == "you"
assert examples[3].data.get(TOKENS_NAMES[ACTION_TEXT])[3].text == "going"
def test_train_tokenizer_action_name(text: Text, expected_tokens: List[Text],
expected_indices: List[Tuple[int]]):
tk = WhitespaceTokenizer()
message = Message.build(text=text)
message.set(ACTION_NAME, text)
training_data = TrainingData()
training_data.training_examples = [message]
tk.train(training_data)
# check action_name attribute
tokens = training_data.training_examples[0].get(TOKENS_NAMES[ACTION_NAME])
assert [t.text for t in tokens] == [text]
def test_count_vector_featurizer_process_by_attribute(
sentence: Text,
action_name: Text,
action_text: Text,
action_name_features: np.ndarray,
response_features: np.ndarray,
):
ftr = CountVectorsFeaturizer({
"token_pattern": r"(?u)\b\w+\b",
"additional_vocabulary_size": {
"text": 0,
"response": 0,
"action_text": 0
},
})
tk = WhitespaceTokenizer()
# add a second example that has some response, so that the vocabulary for
# response exists
train_message = Message(data={TEXT: "hello"})
train_message.set(ACTION_NAME, "greet")
train_message1 = Message(data={TEXT: "hello"})
train_message1.set(ACTION_TEXT, "hi")
data = TrainingData([train_message, train_message1])
tk.train(data)
ftr.train(data)
test_message = Message(data={TEXT: sentence})
test_message.set(ACTION_NAME, action_name)
test_message.set(ACTION_TEXT, action_text)
for module in [tk, ftr]:
module.process(test_message)
action_name_seq_vecs, action_name_sen_vecs = test_message.get_sparse_features(
ACTION_NAME, [])
if action_name_seq_vecs:
action_name_seq_vecs = action_name_seq_vecs.features
if action_name_sen_vecs:
action_name_sen_vecs = action_name_sen_vecs.features
assert action_name_seq_vecs.toarray()[0] == action_name_features
assert action_name_sen_vecs is None
def test_flexible_nlu_pipeline():
message = Message("This is a test message.", data={"intent": "test"})
training_data = TrainingData([message, message, message, message, message])
tokenizer = WhitespaceTokenizer()
tokenizer.train(training_data)
featurizer = CountVectorsFeaturizer(
component_config={FEATURIZER_CLASS_ALIAS: "cvf_word"}
)
featurizer.train(training_data)
featurizer = CountVectorsFeaturizer(
component_config={
FEATURIZER_CLASS_ALIAS: "cvf_char",
"min_ngram": 1,
"max_ngram": 3,
"analyzer": "char_wb",
}
)
featurizer.train(training_data)
featurizer = LexicalSyntacticFeaturizer({})
featurizer.train(training_data)
assert len(message.features) == 4
assert message.features[0].origin == "cvf_word"
# cvf word is also extracted for the intent
assert message.features[1].origin == "cvf_word"
assert message.features[2].origin == "cvf_char"
assert message.features[3].origin == "LexicalSyntacticFeaturizer"
feature_dim = (
message.features[0].features.shape[1] + message.features[3].features.shape[1]
)
classifier = DIETClassifier(
component_config={FEATURIZERS: ["cvf_word", "LexicalSyntacticFeaturizer"]}
)
model_data = classifier.preprocess_train_data(training_data)
assert len(model_data.get("text_features")) == 1
assert len(model_data.get("label_features")) == 1
assert model_data.get("text_features")[0][0].shape == (6, feature_dim)
assert model_data.get("label_features")[0][0].shape == (1, 1)
def test_whitespace_training(supervised_embeddings_config):
examples = [
Message(
"Any Mexican restaurant will do",
{
"intent":
"restaurant_search",
"entities": [{
"start": 4,
"end": 11,
"value": "Mexican",
"entity": "cuisine"
}],
},
),
Message(
"I want Tacos!",
{
"intent":
"restaurant_search",
"entities": [{
"start": 7,
"end": 12,
"value": "Mexican",
"entity": "cuisine"
}],
},
),
]
component_config = {"case_sensitive": False}
tk = WhitespaceTokenizer(component_config)
tk.train(TrainingData(training_examples=examples),
supervised_embeddings_config)
assert examples[0].data.get("tokens")[0].text == "any"
assert examples[0].data.get("tokens")[1].text == "mexican"
assert examples[0].data.get("tokens")[2].text == "restaurant"
assert examples[0].data.get("tokens")[3].text == "will"
assert examples[0].data.get("tokens")[4].text == "do"
assert examples[1].data.get("tokens")[0].text == "i"
assert examples[1].data.get("tokens")[1].text == "want"
assert examples[1].data.get("tokens")[2].text == "tacos"
def test_lm_featurizer_number_of_sub_tokens(text,
expected_number_of_sub_tokens):
config = {
"model_name": "bert",
"model_weights": "bert-base-uncased",
} # Test for one should be enough
lm_featurizer = LanguageModelFeaturizer(config)
whitespace_tokenizer = WhitespaceTokenizer()
message = Message.build(text=text)
td = TrainingData([message])
whitespace_tokenizer.train(td)
lm_featurizer.train(td)
assert [
t.get(NUMBER_OF_SUB_TOKENS) for t in message.get(TOKENS_NAMES[TEXT])
] == expected_number_of_sub_tokens
def test_convert_featurizer_tokens_to_text(sentence: Text, expected_text: Text,
monkeypatch: MonkeyPatch):
tokenizer = WhitespaceTokenizer()
monkeypatch.setattr(ConveRTFeaturizer, "_get_validated_model_url",
lambda x: RESTRICTED_ACCESS_URL)
component_config = {
"name": "ConveRTFeaturizer",
"model_url": RESTRICTED_ACCESS_URL
}
featurizer = ConveRTFeaturizer(component_config)
message = Message.build(text=sentence)
td = TrainingData([message])
tokenizer.train(td)
tokens = featurizer.tokenize(message, attribute=TEXT)
actual_text = ConveRTFeaturizer._tokens_to_text([tokens])[0]
assert expected_text == actual_text
def test_count_vector_featurizer_shared_vocab(sentence, intent, response,
text_features, intent_features,
response_features):
ftr = CountVectorsFeaturizer({
"use_shared_vocab": True,
"additional_vocabulary_size": {
"text": 0,
"response": 0
},
})
tk = WhitespaceTokenizer()
train_message = Message(data={TEXT: sentence})
# this is needed for a valid training example
train_message.set(INTENT, intent)
train_message.set(RESPONSE, response)
data = TrainingData([train_message])
tk.train(data)
ftr.train(data)
seq_vec, sen_vec = train_message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert np.all(seq_vec.toarray()[0] == text_features)
assert sen_vec is not None
seq_vec, sen_vec = train_message.get_sparse_features(INTENT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert np.all(seq_vec.toarray()[0] == intent_features)
assert sen_vec is None
seq_vec, sen_vec = train_message.get_sparse_features(RESPONSE, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert np.all(seq_vec.toarray()[0] == response_features)
assert sen_vec is not None
def test_cvf_incremental_training(
initial_train_text: Text,
additional_train_text: Text,
initial_vocabulary_size: int,
final_vocabulary_size: int,
tmp_path: Path,
):
tk = WhitespaceTokenizer()
initial_cvf = CountVectorsFeaturizer()
train_message = Message(data={"text": initial_train_text})
data = TrainingData([train_message])
tk.train(data)
initial_cvf.train(data)
# Check initial vocabulary size
initial_vocab = initial_cvf.vectorizers["text"].vocabulary_
assert len(initial_vocab) == initial_vocabulary_size
# persist and load initial cvf
file_dict = initial_cvf.persist("ftr", tmp_path)
meta = initial_cvf.component_config.copy()
meta.update(file_dict)
new_cvf = CountVectorsFeaturizer.load(meta, tmp_path, should_finetune=True)
# Check vocabulary size again
assert len(new_cvf.vectorizers["text"].vocabulary_) == initial_vocabulary_size
additional_train_message = Message(data={"text": additional_train_text})
data = TrainingData([train_message, additional_train_message])
tk.train(data)
new_cvf.train(data)
new_vocab = new_cvf.vectorizers["text"].vocabulary_
# Check vocabulary size after finetuning
assert len(new_vocab) == final_vocabulary_size
# Check indices of initial vocabulary haven't changed in the new vocabulary
for vocab_token, vocab_index in initial_vocab.items():
assert vocab_token in new_vocab
assert new_vocab.get(vocab_token) == vocab_index
def test_train_tokenizer_e2e_actions(text: Text, expected_tokens: List[Text],
expected_indices: List[Tuple[int]]):
tk = WhitespaceTokenizer()
message = Message.build(text=text)
message.set(ACTION_TEXT, text)
message.set(ACTION_NAME, text)
training_data = TrainingData()
training_data.training_examples = [message]
tk.train(training_data)
for attribute in [ACTION_TEXT, TEXT]:
tokens = training_data.training_examples[0].get(
TOKENS_NAMES[attribute])
assert [t.text for t in tokens] == expected_tokens
assert [t.start for t in tokens] == [i[0] for i in expected_indices]
assert [t.end for t in tokens] == [i[1] for i in expected_indices]