def test_train_named_model(component_builder):
_config = utilities.base_test_conf("keyword")
_config['name'] = "my_keyword_model"
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
assert persisted_path.strip("/\\").endswith(
"my_keyword_model") # should be saved in a dir named after model
def test_train_model(pipeline_template, component_builder):
_config = utilities.base_test_conf(pipeline_template)
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
component_builder)
assert loaded.pipeline
def test_train_model(pipeline_template, component_builder):
_config = utilities.base_test_conf(pipeline_template)
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello", time=None) is not None
def test_train_model_multithread(pipeline_template, interpreter_builder):
_config = utilities.base_test_conf(pipeline_template)
_config['num_threads'] = 2
(trained, persisted_path) = utilities.run_train(_config)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
interpreter_builder)
assert loaded.pipeline
def test_train_model_noents(component_builder):
_config = utilities.base_test_conf("all_components")
_config['data'] = "./data/examples/rasa/demo-rasa-noents.json"
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello", time=None) is not None
def test_train_model(pipeline_template, component_builder):
_config = utilities.base_test_conf(pipeline_template)
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, 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_backend(backend_name):
_config = base_test_conf(backend_name)
(trained, persisted_path) = utilities.run_train(_config)
assert trained.entity_extractor is not None
assert trained.intent_classifier is not None
loaded = utilities.load_interpreter_for_model(_config, persisted_path)
assert loaded.extractor is not None
assert loaded.classifier is not None
def test_train_model_noents(pipeline_template, interpreter_builder):
_config = utilities.base_test_conf(pipeline_template)
_config['data'] = "./data/examples/rasa/demo-rasa-noents.json"
(trained, persisted_path) = utilities.run_train(_config)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
interpreter_builder)
assert loaded.pipeline
def test_train_model_multithread(component_builder):
_config = utilities.base_test_conf("all_components")
_config['num_threads'] = 2
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello", time=None) is not None
def test_train_model_multithread(component_builder):
_config = utilities.base_test_conf("all_components")
_config['num_threads'] = 2
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
component_builder)
assert loaded.pipeline
assert loaded.parse("hello", time=None) is not None
def test_train_model_noents(component_builder):
_config = utilities.base_test_conf("all_components")
_config['data'] = "./data/examples/mynlu/demo-mynlu-noents.json"
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, 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_backend_noents(backend_name):
_config = base_test_conf(backend_name)
_config['data'] = "./data/examples/rasa/demo-rasa-noents.json"
(trained, persisted_path) = utilities.run_train(_config)
assert trained.entity_extractor is None
assert trained.intent_classifier is not None
loaded = utilities.load_interpreter_for_model(_config, persisted_path)
assert loaded.extractor is None
assert loaded.classifier is not None
def test_train_model_noents(component_builder):
_config = utilities.base_test_conf("all_components")
_config['data'] = "./data/examples/rasa/demo-rasa-noents.json"
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
component_builder)
assert loaded.pipeline
assert loaded.parse("hello", time=None) is not None
def test_train_backend_multithread(backend_name, spacy_nlp_en):
# basic conf
_config = base_test_conf(backend_name)
_config['num_threads'] = 2
(trained, persisted_path) = utilities.run_train(_config)
assert trained.entity_extractor is not None
assert trained.intent_classifier is not None
loaded = utilities.load_interpreter_for_model(spacy_nlp_en, _config,
persisted_path)
assert loaded.extractor is not None
assert loaded.classifier is not None
def test_train_spacy_sklearn_finetune_ner():
# basic conf
_config = base_test_conf(SPACY_BACKEND_NAME)
_config['fine_tune_spacy_ner'] = True
(trained, _) = utilities.run_train(_config)
assert trained.entity_extractor is not None
assert trained.intent_classifier is not None
doc = trained.nlp(u"I am living in New York now.")
entities = trained.entity_extractor.extract_entities(doc)
# Although the model is trained on restaurant entities, we can use the entities (`GPE`, `DATE`)
# from spacy since we are fine tuning. This should even be the case if the rasa-entity training data changes!
assert {
u'start': 15,
u'end': 23,
u'value': u'New York',
u'entity': u'GPE'
} in entities
def test_train_spacy_sklearn_finetune_ner(interpreter_builder):
_config = utilities.base_test_conf("spacy_sklearn")
_config['fine_tune_spacy_ner'] = True
(trained, persisted_path) = utilities.run_train(_config)
assert trained.pipeline
loaded = utilities.load_interpreter_for_model(_config, persisted_path,
interpreter_builder)
result = loaded.parse(u"I am living in New York now.")
entities = result['entities']
# Although the model is trained on restaurant entities, we can use the entities (`GPE`, `DATE`)
# from spacy since we are fine tuning. This should even be the case if the rasa-entity training data changes!
assert {
u'start': 15,
u'end': 23,
u'value': u'New York',
u'entity': u'GPE'
} in entities
def test_handles_pipeline_with_non_existing_component(component_builder):
_config = utilities.base_test_conf("spacy_sklearn")
_config['pipeline'].append("my_made_up_component")
with pytest.raises(Exception) as execinfo:
utilities.run_train(_config, component_builder)
assert "Failed to find component" in str(execinfo.value)
def test_train_model_empty_pipeline(component_builder):
_config = utilities.base_test_conf(
pipeline_template=None) # Should return an empty pipeline
with pytest.raises(ValueError):
utilities.run_train(_config, component_builder)
def test_train_model_empty_pipeline(component_builder):
_config = utilities.base_test_conf(pipeline_template=None) # Should return an empty pipeline
with pytest.raises(ValueError):
utilities.run_train(_config, component_builder)
def test_handles_pipeline_with_non_existing_component(component_builder):
_config = utilities.base_test_conf("spacy_sklearn")
_config['pipeline'].append("my_made_up_component")
with pytest.raises(Exception) as execinfo:
utilities.run_train(_config, component_builder)
assert "Failed to find component" in str(execinfo.value)
def test_train_named_model(component_builder):
_config = utilities.base_test_conf("keyword")
_config['name'] = "my_keyword_model"
(trained, persisted_path) = utilities.run_train(_config, component_builder)
assert trained.pipeline
assert persisted_path.strip("/\\").endswith("my_keyword_model") # should be saved in a dir named after model