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)
intent_vecs = train_message.get_sparse_features(INTENT, [])
response_vecs = train_message.get_sparse_features(RESPONSE, [])
if intent_features:
assert intent_vecs.toarray()[0] == intent_features
else:
assert intent_vecs is None
if response_features:
assert response_vecs.toarray()[0] == response_features
else:
assert response_vecs is None
def test_count_vectors_featurizer_train():
featurizer = CountVectorsFeaturizer.create({}, RasaNLUModelConfig())
sentence = "Hey how are you today ?"
message = Message(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, [])
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, [])
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, [])
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_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
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({}, RasaNLUModelConfig())
sentence = "hey how are you today 19.12.2019 ?"
message = Message(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, [])
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, [])
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, [])
assert seq_vecs is None
assert sen_vec is None
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("")
train_message.set(TOKENS_NAMES[TEXT], tokens_feature)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message("")
test_message.set(TOKENS_NAMES[TEXT], tokens_feature)
ftr.process(test_message)
seq_vec, sen_vec = train_message.get_sparse_features(TEXT, [])
assert np.all(seq_vec.toarray()[0] == expected)
assert sen_vec is not None
def test_text_featurizer(sentence, expected_features):
featurizer = LexicalSyntacticFeaturizer({
"features": [
["BOS", "upper"],
["BOS", "EOS", "prefix2", "digit"],
["EOS", "low"],
]
})
train_message = Message(sentence)
test_message = Message(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, [])
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_oov_token(sentence, expected):
ftr = CountVectorsFeaturizer({"OOV_token": "__oov__"})
train_message = Message(sentence)
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
seq_vec, sen_vec = train_message.get_sparse_features(TEXT, [])
assert np.all(seq_vec.toarray()[0] == expected)
assert sen_vec is not None
def test_get_sparse_features(
features: Optional[List[Features]],
attribute: Text,
featurizers: List[Text],
expected_features: Optional[List[Features]],
):
message = Message("This is a test sentence.", features=features)
actual_features = message.get_sparse_features(attribute, featurizers)
if expected_features is None:
assert actual_features is None
else:
assert np.all(actual_features.toarray() == expected_features)
def test_count_vector_featurizer_oov_words(sentence, expected):
ftr = CountVectorsFeaturizer({
"token_pattern": r"(?u)\b\w+\b",
"OOV_token": "__oov__",
"OOV_words": ["oov_word0", "OOV_word1"],
})
train_message = Message(sentence)
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
vec = train_message.get_sparse_features(TEXT, [])
assert np.all(vec.toarray()[0] == expected)
def test_count_vector_featurizer_char(sentence, expected):
ftr = CountVectorsFeaturizer({
"min_ngram": 1,
"max_ngram": 2,
"analyzer": "char"
})
train_message = Message(sentence)
WhitespaceTokenizer().process(train_message)
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
WhitespaceTokenizer().process(test_message)
ftr.process(test_message)
vec = train_message.get_sparse_features(TEXT, [])
assert np.all(vec.toarray()[0] == expected)
def test_text_featurizer_window_size(sentence, expected, expected_cls):
featurizer = LexicalSyntacticFeaturizer(
{"features": [["upper"], ["digit"], ["low"], ["digit"]]})
train_message = Message(sentence)
test_message = Message(sentence)
WhitespaceTokenizer().process(train_message)
WhitespaceTokenizer().process(test_message)
featurizer.train(TrainingData([train_message]))
featurizer.process(test_message)
actual = test_message.get_sparse_features(TEXT, [])
assert isinstance(actual, scipy.sparse.coo_matrix)
assert np.all(actual.toarray()[0] == expected)
assert np.all(actual.toarray()[-1] == expected_cls)
def test_count_vector_featurizer(sentence, expected, expected_cls):
ftr = CountVectorsFeaturizer({"token_pattern": r"(?u)\b\w+\b"})
train_message = Message(sentence)
test_message = Message(sentence)
WhitespaceTokenizer().process(train_message)
WhitespaceTokenizer().process(test_message)
ftr.train(TrainingData([train_message]))
ftr.process(test_message)
vecs = test_message.get_sparse_features(TEXT, [])
assert isinstance(vecs, scipy.sparse.coo_matrix)
actual_vecs = vecs.toarray()
assert np.all(actual_vecs[0] == expected)
assert np.all(actual_vecs[-1] == expected_cls)
def test_text_featurizer_using_pos(sentence, expected, spacy_nlp):
featurizer = LexicalSyntacticFeaturizer({"features": [["pos", "pos2"]]})
train_message = Message(sentence)
test_message = Message(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)
actual = test_message.get_sparse_features(TEXT, [])
assert isinstance(actual, scipy.sparse.coo_matrix)
assert np.all(actual.toarray() == expected)
def test_count_vector_featurizer_persist_load(tmp_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(sentence1)
train_message2 = Message(sentence2)
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))
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(sentence1)
test_ftr.process(test_message1)
test_message2 = Message(sentence2)
test_ftr.process(test_message2)
test_seq_vec_1, test_sen_vec_1 = test_message1.get_sparse_features(
TEXT, [])
train_seq_vec_1, train_sen_vec_1 = train_message1.get_sparse_features(
TEXT, [])
test_seq_vec_2, test_sen_vec_2 = test_message2.get_sparse_features(
TEXT, [])
train_seq_vec_2, train_sen_vec_2 = train_message2.get_sparse_features(
TEXT, [])
# 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())
