def test_count_vector_featurizer_using_tokens(tokens, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
# 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", tokens_feature)
train_message.set("intent", "bla") # this is needed for a valid training example
data = TrainingData([train_message])
ftr.train(data)
test_message = Message("")
test_message.set("tokens", tokens_feature)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer_using_tokens(tokens, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
# 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", tokens_feature)
# this is needed for a valid training example
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message("")
test_message.set("tokens", tokens_feature)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
message = Message(sentence)
message.set("intent", "bla")
data = TrainingData([message])
ftr.train(data)
ftr.process(message)
assert np.all(message.get("text_features")[0] == expected)
def test_count_vector_featurizer(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
message = Message(sentence)
message.set("intent", "bla")
data = TrainingData([message])
ftr.train(data)
ftr.process(message)
assert np.all(message.get("text_features")[0] == expected)
def test_count_vector_featurizer(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer_oov_token(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b',
"OOV_token": '__oov__'})
train_message = Message(sentence)
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"token_pattern": r'(?u)\b\w+\b'})
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer_oov_token(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({
"token_pattern": r'(?u)\b\w+\b',
"OOV_token": '__oov__'
})
train_message = Message(sentence)
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)
def test_count_vector_featurizer(sentence, expected):
from rasa_nlu.featurizers.count_vectors_featurizer import \
CountVectorsFeaturizer
ftr = CountVectorsFeaturizer({"min_ngram": 1,
"max_ngram": 2,
"analyzer": 'char'})
train_message = Message(sentence)
# this is needed for a valid training example
train_message.set("intent", "bla")
data = TrainingData([train_message])
ftr.train(data)
test_message = Message(sentence)
ftr.process(test_message)
assert np.all(test_message.get("text_features") == expected)