def test_handles_pipeline_with_non_existing_component(component_builder):
_config = utilities.base_test_conf("pretrained_embeddings_spacy")
_config.pipeline.append({"name": "my_made_up_component"})
with pytest.raises(Exception) as execinfo:
train(_config, data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert "Failed to find component" in str(execinfo.value)
def test_train_model_empty_pipeline(component_builder):
# Should return an empty pipeline
_config = utilities.base_test_conf(pipeline_template=None)
with pytest.raises(ValueError):
train(_config,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
def save_nlu_data(data, path, append=True):
write_mode = 'w'
if append:
write_mode = 'a'
nlu_text = ""
with open(path, write_mode) as nlu_data:
for intent, texts in data['intent'].items():
nlu_text += '\n## intent:' + intent + '\n'
for text in texts:
nlu_text += '- ' + text + '\n'
for intent, texts in data['lookup'].items():
nlu_text += '\n## lookup:' + intent + '\n'
for text in texts:
nlu_text += '- ' + text + '\n'
for intent, texts in data['synonym'].items():
nlu_text += '\n## synonym:' + intent + '\n'
for text in texts:
nlu_text += '- ' + text + '\n'
nlu_data.write(nlu_text)
train(RASA_CONFIG_PATH, RASA_NLU_DATA_PATH, RASA_MODEL_SAVE_PATH,
'current', 'model')
global interpreter
interpreter = Interpreter.load(NLU_MODEL_PATH)
def test_handles_pipeline_with_non_existing_component(component_builder):
_config = utilities.base_test_conf("pretrained_embeddings_spacy")
_config.pipeline.append({"name": "my_made_up_component"})
with pytest.raises(Exception) as execinfo:
train(_config, data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert "Failed to find component" in str(execinfo.value)
def test_train_model_empty_pipeline(component_builder):
# Should return an empty pipeline
_config = utilities.base_test_conf(pipeline_template=None)
with pytest.raises(ValueError):
train(
_config,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
def train_nlu(config: Text, nlu_data: Text, output: Text,
train_path: Optional[Text]) -> Optional["Interpreter"]:
"""Trains a NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa_nlu
_train_path = train_path or tempfile.mkdtemp()
_, nlu_model, _ = rasa_nlu.train(config, nlu_data, _train_path,
project="",
fixed_model_name="nlu")
if not train_path:
nlu_data = data.get_nlu_directory(nlu_data)
output_path = create_output_path(output, prefix="nlu-")
new_fingerprint = model.model_fingerprint(config, nlu_data=nlu_data)
model.create_package_rasa(_train_path, output_path, new_fingerprint)
print_success("Your Rasa NLU model is trained and saved at '{}'."
"".format(output_path))
return nlu_model
def test_train_model(pipeline_template, component_builder, tmpdir):
_config = utilities.base_test_conf(pipeline_template)
(trained, _, persisted_path) = train(_config,
path=tmpdir.strpath,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_named_model(component_builder, tmpdir):
_config = utilities.base_test_conf("keyword")
(trained, _, persisted_path) = train(_config,
path=tmpdir.strpath,
project="my_keyword_model",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert trained.pipeline
normalized_path = os.path.dirname(os.path.normpath(persisted_path))
# should be saved in a dir named after a project
assert os.path.basename(normalized_path) == "my_keyword_model"
def test_train_model_noents(language, pipeline, component_builder, tmpdir):
_config = RasaNLUModelConfig({"pipeline": pipeline, "language": language})
(trained, _,
persisted_path) = train(_config,
path=tmpdir.strpath,
data="./data/test/demo-rasa-noents.json",
component_builder=component_builder)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_model_on_test_pipelines(language, pipeline, component_builder,
tmpdir):
_config = RasaNLUModelConfig({"pipeline": pipeline, "language": language})
(trained, _, persisted_path) = train(_config,
path=tmpdir.strpath,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_model(pipeline_template, component_builder, tmpdir):
_config = utilities.base_test_conf(pipeline_template)
(trained, _, persisted_path) = train(
_config,
path=tmpdir.strpath,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_named_model(component_builder, tmpdir):
_config = utilities.base_test_conf("keyword")
(trained, _, persisted_path) = train(
_config,
path=tmpdir.strpath,
project="my_keyword_model",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert trained.pipeline
normalized_path = os.path.dirname(os.path.normpath(persisted_path))
# should be saved in a dir named after a project
assert os.path.basename(normalized_path) == "my_keyword_model"
def test_train_model_noents(language, pipeline, component_builder, tmpdir):
_config = RasaNLUModelConfig({"pipeline": pipeline, "language": language})
(trained, _, persisted_path) = train(
_config,
path=tmpdir.strpath,
data="./data/test/demo-rasa-noents.json",
component_builder=component_builder)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_model_multithread(language, pipeline, component_builder, tmpdir):
_config = RasaNLUModelConfig({"pipeline": pipeline, "language": language})
(trained, _, persisted_path) = train(
_config,
path=tmpdir.strpath,
data=DEFAULT_DATA_PATH,
component_builder=component_builder,
num_threads=2)
assert trained.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_random_seed(component_builder, tmpdir):
"""test if train result is the same for two runs of tf embedding"""
_config = utilities.base_test_conf("supervised_embeddings")
# set fixed random seed to 1
_config.set_component_attr(5, random_seed=1)
# first run
(trained_a, _,
persisted_path_a) = train(_config,
path=tmpdir.strpath + "_a",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
# second run
(trained_b, _,
persisted_path_b) = train(_config,
path=tmpdir.strpath + "_b",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
loaded_a = Interpreter.load(persisted_path_a, component_builder)
loaded_b = Interpreter.load(persisted_path_b, component_builder)
result_a = loaded_a.parse("hello")["intent"]["confidence"]
result_b = loaded_b.parse("hello")["intent"]["confidence"]
assert result_a == result_b
def test_random_seed(component_builder, tmpdir):
"""test if train result is the same for two runs of tf embedding"""
_config = utilities.base_test_conf("supervised_embeddings")
# set fixed random seed to 1
_config.set_component_attr(5, random_seed=1)
# first run
(trained_a, _, persisted_path_a) = train(
_config,
path=tmpdir.strpath + "_a",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
# second run
(trained_b, _, persisted_path_b) = train(
_config,
path=tmpdir.strpath + "_b",
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
loaded_a = Interpreter.load(persisted_path_a, component_builder)
loaded_b = Interpreter.load(persisted_path_b, component_builder)
result_a = loaded_a.parse("hello")["intent"]["confidence"]
result_b = loaded_b.parse("hello")["intent"]["confidence"]
assert result_a == result_b