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_count_vectors_featurizer_train(
create_featurizer: Callable[..., CountVectorsFeaturizerGraphComponent],
whitespace_tokenizer: WhitespaceTokenizerGraphComponent,
):
featurizer = create_featurizer()
sentence = "Hey how are you today ?"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
whitespace_tokenizer.process_training_data(TrainingData([message]))
data = TrainingData([message])
featurizer.train(data)
featurizer.process_training_data(data)
expected = np.array([0, 1, 0, 0, 0])
expected_cls = np.array([1, 1, 1, 1, 1])
seq_vec, sen_vec = message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert (5, 5) == seq_vec.shape
assert (1, 5) == sen_vec.shape
assert np.all(seq_vec.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vec, sen_vec = message.get_sparse_features(RESPONSE, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert (5, 5) == seq_vec.shape
assert (1, 5) == sen_vec.shape
assert np.all(seq_vec.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vec, sen_vec = message.get_sparse_features(INTENT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert sen_vec is None
assert (1, 1) == seq_vec.shape
assert np.all(seq_vec.toarray()[0] == np.array([1]))
def test_count_vectors_featurizer_train():
featurizer = CountVectorsFeaturizer.create(
{"additional_vocabulary_size": {
"text": 0,
"response": 0
}}, RasaNLUModelConfig())
sentence = "Hey how are you today ?"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
WhitespaceTokenizer().train(TrainingData([message]))
featurizer.train(TrainingData([message]), RasaNLUModelConfig())
expected = np.array([0, 1, 0, 0, 0])
expected_cls = np.array([1, 1, 1, 1, 1])
seq_vec, sen_vec = message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert (5, 5) == seq_vec.shape
assert (1, 5) == sen_vec.shape
assert np.all(seq_vec.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vec, sen_vec = message.get_sparse_features(RESPONSE, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert (5, 5) == seq_vec.shape
assert (1, 5) == sen_vec.shape
assert np.all(seq_vec.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vec, sen_vec = message.get_sparse_features(INTENT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert sen_vec is None
assert (1, 1) == seq_vec.shape
assert np.all(seq_vec.toarray()[0] == np.array([1]))
def test_count_vector_featurizer_char(
sentence: Text,
expected: List[List[int]],
create_featurizer: Callable[..., CountVectorsFeaturizer],
whitespace_tokenizer: WhitespaceTokenizer,
):
ftr = create_featurizer({
"min_ngram": 1,
"max_ngram": 2,
"analyzer": "char",
})
train_message = Message(data={TEXT: sentence})
whitespace_tokenizer.process([train_message])
data = TrainingData([train_message])
ftr.train(data)
ftr.process_training_data(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] == expected)
assert sen_vec is not None
def test_text_featurizer_using_pos(sentence, expected, spacy_nlp):
featurizer = LexicalSyntacticFeaturizer({"features": [["pos", "pos2"]]})
train_message = Message(data={TEXT: sentence})
test_message = Message(data={TEXT: sentence})
train_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
test_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
SpacyTokenizer().process(train_message)
SpacyTokenizer().process(test_message)
featurizer.train(TrainingData([train_message]))
featurizer.process(test_message)
seq_vec, sen_vec = test_message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert isinstance(seq_vec, scipy.sparse.coo_matrix)
assert sen_vec is None
assert np.all(seq_vec.toarray() == expected)
def print_message(message: Message) -> None:
features = {**message.as_dict_nlu()}
seq_vecs, sen_vecs = message.get_dense_features(TEXT)
features["dense"] = {
"sequence": None if not seq_vecs else dense_message(seq_vecs.features),
"sentence": None if not sen_vecs else dense_message(sen_vecs.features),
}
seq_vecs, sen_vecs = message.get_sparse_features(TEXT)
features["sparse"] = {
"sequence":
None if not seq_vecs else sparse_message(seq_vecs.features),
"sentence":
None if not sen_vecs else sparse_message(sen_vecs.features),
}
if "text_tokens" in features.keys():
features["text_tokens"] = [t.text for t in features["text_tokens"]]
if "intent" in features.keys():
features["intent"] = {
k: v
for k, v in features["intent"].items() if "id" != k
}
if "intent_ranking" in features.keys():
features["intent_ranking"] = [{
k: v
for k, v in i.items() if "id" != k
} for i in features["intent_ranking"]]
if "diagnostic_data" in features.keys():
features["diagnostic_data"] = {
name: {k: dense_message(v)
for k, v in comp.items()}
for name, comp in features["diagnostic_data"].items()
}
print(features)
def test_get_sparse_features(
features: Optional[List[Features]],
attribute: Text,
featurizers: List[Text],
expected_seq_features: Optional[List[Features]],
expected_sen_features: Optional[List[Features]],
):
message = Message(data={TEXT: "This is a test sentence."},
features=features)
actual_seq_features, actual_sen_features = message.get_sparse_features(
attribute, featurizers)
if actual_seq_features:
actual_seq_features = actual_seq_features.features
if actual_sen_features:
actual_sen_features = actual_sen_features.features
if expected_seq_features is None:
assert actual_seq_features is None
else:
assert actual_seq_features is not None
assert np.all(actual_seq_features.toarray() == expected_seq_features)
if expected_sen_features is None:
assert actual_sen_features is None
else:
assert actual_sen_features is not None
assert np.all(actual_sen_features.toarray() == expected_sen_features)
def test_count_vector_featurizer(
sentence: Text,
expected: List[List[int]],
expected_cls: List[List[int]],
create_featurizer: Callable[..., CountVectorsFeaturizerGraphComponent],
whitespace_tokenizer: WhitespaceTokenizerGraphComponent,
):
ftr = create_featurizer()
train_message = Message(data={TEXT: sentence})
test_message = Message(data={TEXT: sentence})
whitespace_tokenizer.process([train_message])
whitespace_tokenizer.process([test_message])
ftr.train(TrainingData([train_message]))
ftr.process([test_message])
seq_vecs, sen_vecs = test_message.get_sparse_features(TEXT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vecs:
sen_vecs = sen_vecs.features
assert isinstance(seq_vecs, scipy.sparse.coo_matrix)
assert isinstance(sen_vecs, scipy.sparse.coo_matrix)
actual_seq_vecs = seq_vecs.toarray()
actual_sen_vecs = sen_vecs.toarray()
assert np.all(actual_seq_vecs[0] == expected)
assert np.all(actual_sen_vecs[-1] == expected_cls)
def test_count_vector_featurizer(sentence, expected, expected_cls):
ftr = CountVectorsFeaturizer()
train_message = Message(data={TEXT: sentence})
test_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(train_message)
WhitespaceTokenizer().process(test_message)
ftr.train(TrainingData([train_message]))
ftr.process(test_message)
seq_vecs, sen_vecs = test_message.get_sparse_features(TEXT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vecs:
sen_vecs = sen_vecs.features
assert isinstance(seq_vecs, scipy.sparse.coo_matrix)
assert isinstance(sen_vecs, scipy.sparse.coo_matrix)
actual_seq_vecs = seq_vecs.toarray()
actual_sen_vecs = sen_vecs.toarray()
assert np.all(actual_seq_vecs[0] == expected)
assert np.all(actual_sen_vecs[-1] == expected_cls)
def test_text_featurizer(sentence, expected_features):
featurizer = LexicalSyntacticFeaturizer({
"features": [
["BOS", "upper"],
["BOS", "EOS", "prefix2", "digit"],
["EOS", "low"],
]
})
train_message = Message(data={TEXT: sentence})
test_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(train_message)
WhitespaceTokenizer().process(test_message)
featurizer.train(TrainingData([train_message]))
featurizer.process(test_message)
seq_vec, sen_vec = test_message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert isinstance(seq_vec, scipy.sparse.coo_matrix)
assert sen_vec is None
assert np.all(seq_vec.toarray() == expected_features[:-1])
def test_count_vector_featurizer_using_tokens(tokens, expected):
ftr = CountVectorsFeaturizer()
# using empty string instead of real text string to make sure
# count vector only can come from `tokens` feature.
# using `message.text` can not get correct result
tokens_feature = [Token(i, 0) for i in tokens]
train_message = Message(data={TEXT: ""})
train_message.set(TOKENS_NAMES[TEXT], tokens_feature)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(data={TEXT: ""})
test_message.set(TOKENS_NAMES[TEXT], tokens_feature)
ftr.process(test_message)
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] == expected)
assert sen_vec is not None
def test_count_vector_featurizer_use_lemma(
spacy_nlp: Any,
sentence: Text,
sequence_features: List[List[int]],
sentence_features: List[List[int]],
use_lemma: bool,
):
ftr = CountVectorsFeaturizer({"use_lemma": use_lemma})
train_message = Message(data={TEXT: sentence})
train_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
test_message = Message(data={TEXT: sentence})
test_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
SpacyTokenizer().process(train_message)
SpacyTokenizer().process(test_message)
ftr.train(TrainingData([train_message]))
ftr.process(test_message)
seq_vecs, sen_vecs = test_message.get_sparse_features(TEXT, [])
assert isinstance(seq_vecs.features, scipy.sparse.coo_matrix)
assert isinstance(sen_vecs.features, scipy.sparse.coo_matrix)
actual_seq_vecs = seq_vecs.features.toarray()
actual_sen_vecs = sen_vecs.features.toarray()
assert np.all(actual_seq_vecs[0] == sequence_features)
assert np.all(actual_sen_vecs[-1] == sentence_features)
def test_count_vector_featurizer_oov_words(sentence, expected):
ftr = CountVectorsFeaturizer({
"OOV_token": "__oov__",
"OOV_words": ["oov_word0", "OOV_word1"],
"additional_vocabulary_size": {
"text": 0
},
})
train_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(data={TEXT: sentence})
ftr.process(test_message)
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] == expected)
assert sen_vec is not None
def test_count_vector_featurizer_char(sentence, expected):
ftr = CountVectorsFeaturizer({
"min_ngram": 1,
"max_ngram": 2,
"analyzer": "char",
"additional_vocabulary_size": {
"text": 0
},
})
train_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(test_message)
ftr.process(test_message)
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] == expected)
assert sen_vec is not None
def test_count_vector_featurizer_attribute_featurization(
sentence: Text,
intent: Text,
response: Optional[Text],
intent_features: List[List[int]],
response_features: Optional[List[List[int]]],
create_featurizer: Callable[..., CountVectorsFeaturizerGraphComponent],
whitespace_tokenizer: WhitespaceTokenizerGraphComponent,
):
ftr = create_featurizer()
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])
whitespace_tokenizer.process_training_data(data)
ftr.train(data)
ftr.process_training_data(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 fetch_sparse_features(txt, tokenizer, featurizer):
message = Message(
{TEXT: "my advices include to give advice and giving many greetings"})
tokenizer.process(message)
featurizer.train(TrainingData([message]))
featurizer.process(message)
seq_vecs, sen_vecs = message.get_sparse_features(TEXT, [])
return seq_vecs.features.toarray()
def test_count_vector_featurizer_shared_vocab(
sentence: Text,
intent: Text,
response: Text,
text_features: List[List[int]],
intent_features: List[List[int]],
response_features: List[List[int]],
create_featurizer: Callable[..., CountVectorsFeaturizer],
whitespace_tokenizer: WhitespaceTokenizer,
):
ftr = create_featurizer({
"use_shared_vocab": True,
})
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])
whitespace_tokenizer.process_training_data(data)
ftr.train(data)
ftr.process_training_data(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_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_count_vector_featurizer_attribute_featurization(
sentence, intent, response, intent_features, response_features
):
ftr = CountVectorsFeaturizer()
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)
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 _get_sentence_features(message: Message) -> scipy.sparse.spmatrix:
_, dense_sentence_features = message.get_dense_features(TEXT)
if dense_sentence_features is not None:
rasa.shared.utils.io.raise_warning(
"Dense features are being computed but not used in "
"the SparseNaiveBayesIntentClassifier.")
_, sentence_features = message.get_sparse_features(TEXT)
if sentence_features is not None:
return sentence_features.features
raise ValueError("No sparse sentence features present. "
"Not able to train sklearn intent classifier.")
def test_text_featurizer_using_pos_with_action_text(sentence: Text,
expected: np.ndarray,
spacy_nlp):
featurizer = LexicalSyntacticFeaturizer({"features": [["pos", "pos2"]]})
train_message = Message(data={TEXT: sentence, ACTION_TEXT: sentence})
test_message = Message(data={TEXT: sentence, ACTION_TEXT: sentence})
train_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
train_message.set(SPACY_DOCS[ACTION_TEXT], spacy_nlp(sentence))
test_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
test_message.set(SPACY_DOCS[ACTION_TEXT], spacy_nlp(sentence))
SpacyTokenizer().process(train_message)
SpacyTokenizer().process(test_message)
featurizer.train(TrainingData([train_message]))
# Checking that text is processed as expected
featurizer.process(test_message)
seq_vec, sen_vec = test_message.get_sparse_features(TEXT, [])
if seq_vec:
seq_vec = seq_vec.features
if sen_vec:
sen_vec = sen_vec.features
assert isinstance(seq_vec, scipy.sparse.coo_matrix)
assert sen_vec is None
assert np.all(seq_vec.toarray() == expected)
# Checking that action_text does not get processed and passing attribute works
featurizer.process(test_message)
seq_vec, sen_vec = test_message.get_sparse_features(ACTION_TEXT, [])
assert seq_vec is None
assert sen_vec is None
def fetch_sparse_features(txt, tokenizer, featurizer):
message = Message({TEXT: txt})
tokenizer.process(message)
featurizer.train(TrainingData([message]))
featurizer.process(message)
seq_vecs, sen_vecs = message.get_sparse_features(TEXT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vecs:
sen_vecs = sen_vecs.features
return seq_vecs.toarray()
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_count_vector_featurizer_oov_token(sentence, expected):
ftr = CountVectorsFeaturizer({"OOV_token": "__oov__"})
train_message = Message(data={TEXT: sentence})
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(data={TEXT: sentence})
ftr.process(test_message)
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] == expected)
assert sen_vec is not None
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,
create_featurizer: Callable[..., CountVectorsFeaturizer],
whitespace_tokenizer: WhitespaceTokenizer,
):
ftr = create_featurizer({
"token_pattern": r"(?u)\b\w+\b",
})
# 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])
whitespace_tokenizer.process_training_data(data)
ftr.train(data)
test_message = Message(data={TEXT: sentence})
test_message.set(ACTION_NAME, action_name)
test_message.set(ACTION_TEXT, action_text)
whitespace_tokenizer.process([test_message])
ftr.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_count_vector_featurizer_use_lemma(
spacy_nlp: Any,
sentence: Text,
sequence_features: List[List[int]],
sentence_features: List[List[int]],
use_lemma: bool,
create_featurizer: Callable[..., CountVectorsFeaturizer],
load_featurizer: Callable[..., CountVectorsFeaturizer],
spacy_tokenizer: SpacyTokenizer,
):
config = {
"use_lemma": use_lemma,
"OOV_words": ["drinks"],
"OOV_token": "OOV"
}
ftr = create_featurizer(config)
train_message = Message(data={TEXT: sentence})
train_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
test_message = Message(data={TEXT: sentence})
test_message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
spacy_tokenizer.process([train_message])
spacy_tokenizer.process([test_message])
ftr.train(TrainingData([train_message]), model=SpacyModel(spacy_nlp, "en"))
ftr.process([test_message])
seq_vecs, sen_vecs = test_message.get_sparse_features(TEXT, [])
assert isinstance(seq_vecs.features, scipy.sparse.coo_matrix)
assert isinstance(sen_vecs.features, scipy.sparse.coo_matrix)
actual_seq_vecs = seq_vecs.features.toarray()
actual_sen_vecs = sen_vecs.features.toarray()
assert np.all(actual_seq_vecs[0] == sequence_features)
assert np.all(actual_sen_vecs[-1] == sentence_features)
loaded = load_featurizer(config)
assert loaded.OOV_words == ftr.OOV_words
def test_count_vector_featurizer_oov_token(
sentence: Text,
expected: List[List[int]],
create_featurizer: Callable[..., CountVectorsFeaturizerGraphComponent],
whitespace_tokenizer: WhitespaceTokenizerGraphComponent,
):
ftr = create_featurizer({"OOV_token": "__oov__"})
train_message = Message(data={TEXT: sentence})
whitespace_tokenizer.process([train_message])
data = TrainingData([train_message])
ftr.train(data)
ftr.process_training_data(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] == expected)
assert sen_vec is not None
def test_incremental_train_featurization(tmp_path: Path):
patterns = [
{
"pattern": "[0-9]+",
"name": "number",
"usage": "intent"
},
{
"pattern": "\\bhey*",
"name": "hello",
"usage": "intent"
},
{
"pattern": "[0-1]+",
"name": "binary",
"usage": "intent"
},
]
featurizer = RegexFeaturizer.create({"number_additional_patterns": 5},
RasaNLUModelConfig())
sentence = "hey how are you today 19.12.2019 ?"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
WhitespaceTokenizer().train(TrainingData([message]))
featurizer.train(TrainingData([message], regex_features=patterns),
RasaNLUModelConfig())
# Test featurization of message
expected = np.array([0, 1, 0, 0, 0, 0, 0, 0])
expected_cls = np.array([1, 1, 1, 0, 0, 0, 0, 0])
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 (6, 8) == seq_vecs.shape
assert (1, 8) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
persist_value = featurizer.persist("ftr", str(tmp_path))
loaded_featurizer = RegexFeaturizer.load(
meta={
"number_additional_patterns": 5,
"file": persist_value["file"],
},
should_finetune=True,
model_dir=str(tmp_path),
)
new_patterns = [
{
"pattern": "\\btoday*",
"name": "day",
"usage": "intent"
},
{
"pattern": "\\bhey+",
"name": "hello",
"usage": "intent"
},
]
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
WhitespaceTokenizer().train(TrainingData([message]))
loaded_featurizer.train(
TrainingData([message], regex_features=patterns + new_patterns),
RasaNLUModelConfig(),
)
# Test featurization of message, this time for the extra pattern as well.
expected_token_1 = np.array([0, 1, 0, 0, 0, 0, 0, 0])
expected_token_2 = np.array([0, 0, 0, 1, 0, 0, 0, 0])
expected_cls = np.array([1, 1, 1, 1, 0, 0, 0, 0])
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 (6, 8) == seq_vecs.shape
assert (1, 8) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected_token_1)
assert np.all(seq_vecs.toarray()[-2] == expected_token_2)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
# 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]]
def test_regex_featurizer_train():
patterns = [
{
"pattern": "[0-9]+",
"name": "number",
"usage": "intent"
},
{
"pattern": "\\bhey*",
"name": "hello",
"usage": "intent"
},
{
"pattern": "[0-1]+",
"name": "binary",
"usage": "intent"
},
]
featurizer = RegexFeaturizer.create({"number_additional_patterns": 0},
RasaNLUModelConfig())
sentence = "hey how are you today 19.12.2019 ?"
message = Message(data={TEXT: sentence})
message.set(RESPONSE, sentence)
message.set(INTENT, "intent")
WhitespaceTokenizer().train(TrainingData([message]))
featurizer.train(TrainingData([message], regex_features=patterns),
RasaNLUModelConfig())
expected = np.array([0, 1, 0])
expected_cls = np.array([1, 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 (6, 3) == seq_vecs.shape
assert (1, 3) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vecs, sen_vec = message.get_sparse_features(RESPONSE, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vec:
sen_vec = sen_vec.features
assert (6, 3) == seq_vecs.shape
assert (1, 3) == sen_vec.shape
assert np.all(seq_vecs.toarray()[0] == expected)
assert np.all(sen_vec.toarray()[-1] == expected_cls)
seq_vecs, sen_vec = message.get_sparse_features(INTENT, [])
if seq_vecs:
seq_vecs = seq_vecs.features
if sen_vec:
sen_vec = sen_vec.features
assert seq_vecs is None
assert sen_vec is None
def test_count_vector_featurizer_persist_load(tmp_path: Path):
# set non default values to config
config = {
"analyzer": "char",
"strip_accents": "ascii",
"stop_words": "stop",
"min_df": 2,
"max_df": 3,
"min_ngram": 2,
"max_ngram": 3,
"max_features": 10,
"lowercase": False,
}
train_ftr = CountVectorsFeaturizer(config)
sentence1 = "ababab 123 13xc лаомтгцу sfjv oö aà"
sentence2 = "abababalidcn 123123 13xcdc лаомтгцу sfjv oö aà"
train_message1 = Message(data={TEXT: sentence1})
train_message2 = Message(data={TEXT: sentence2})
WhitespaceTokenizer().process(train_message1)
WhitespaceTokenizer().process(train_message2)
data = TrainingData([train_message1, train_message2])
train_ftr.train(data)
# persist featurizer
file_dict = train_ftr.persist("ftr", str(tmp_path))
train_vect_params = {
attribute: vectorizer.get_params()
for attribute, vectorizer in train_ftr.vectorizers.items()
}
# add trained vocabulary to vectorizer params
for attribute, attribute_vect_params in train_vect_params.items():
if hasattr(train_ftr.vectorizers[attribute], "vocabulary_"):
train_vect_params[attribute].update(
{"vocabulary": train_ftr.vectorizers[attribute].vocabulary_}
)
# load featurizer
meta = train_ftr.component_config.copy()
meta.update(file_dict)
test_ftr = CountVectorsFeaturizer.load(meta, str(tmp_path), finetune_mode=False)
test_vect_params = {
attribute: vectorizer.get_params()
for attribute, vectorizer in test_ftr.vectorizers.items()
}
assert train_vect_params == test_vect_params
# check if vocaculary was loaded correctly
assert hasattr(test_ftr.vectorizers[TEXT], "vocabulary_")
test_message1 = Message(data={TEXT: sentence1})
WhitespaceTokenizer().process(test_message1)
test_ftr.process(test_message1)
test_message2 = Message(data={TEXT: sentence2})
WhitespaceTokenizer().process(test_message2)
test_ftr.process(test_message2)
test_seq_vec_1, test_sen_vec_1 = test_message1.get_sparse_features(TEXT, [])
if test_seq_vec_1:
test_seq_vec_1 = test_seq_vec_1.features
if test_sen_vec_1:
test_sen_vec_1 = test_sen_vec_1.features
train_seq_vec_1, train_sen_vec_1 = train_message1.get_sparse_features(TEXT, [])
if train_seq_vec_1:
train_seq_vec_1 = train_seq_vec_1.features
if train_sen_vec_1:
train_sen_vec_1 = train_sen_vec_1.features
test_seq_vec_2, test_sen_vec_2 = test_message2.get_sparse_features(TEXT, [])
if test_seq_vec_2:
test_seq_vec_2 = test_seq_vec_2.features
if test_sen_vec_2:
test_sen_vec_2 = test_sen_vec_2.features
train_seq_vec_2, train_sen_vec_2 = train_message2.get_sparse_features(TEXT, [])
if train_seq_vec_2:
train_seq_vec_2 = train_seq_vec_2.features
if train_sen_vec_2:
train_sen_vec_2 = train_sen_vec_2.features
# check that train features and test features after loading are the same
assert np.all(test_seq_vec_1.toarray() == train_seq_vec_1.toarray())
assert np.all(test_sen_vec_1.toarray() == train_sen_vec_1.toarray())
assert np.all(test_seq_vec_2.toarray() == train_seq_vec_2.toarray())
assert np.all(test_sen_vec_2.toarray() == train_sen_vec_2.toarray())
