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.do_train(
_config,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
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_named_model(component_builder):
_config = utilities.base_test_conf("keyword")
_config['project'] = "my_keyword_model"
(trained, persisted_path) = utilities.run_train(_config, 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_load_and_persist_without_train(component_builder):
_config = utilities.base_test_conf("all_components")
trainer = Trainer(_config, component_builder)
persistor = create_persistor(_config)
persisted_path = trainer.persist(_config['path'], persistor, project_name=_config['name'])
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_model_noents(component_builder):
_config = utilities.base_test_conf("all_components")
_config['data'] = "./data/test/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") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_duckling_entity_extractor_and_synonyms(component_builder):
_config = utilities.base_test_conf("all_components")
_config["duckling_dimensions"] = ["number"]
duckling = component_builder.create_component("ner_duckling", _config)
synonyms = component_builder.create_component("ner_synonyms", _config)
message = Message("He was 6 feet away")
duckling.process(message)
synonyms.process(message) # checks that the synonym processor can handle entities that have int values
assert message is not None
def test_duckling_entity_extractor_and_synonyms(component_builder):
_config = utilities.base_test_conf("all_components")
_config["duckling_dimensions"] = ["number"]
duckling = component_builder.create_component("ner_duckling", _config)
synonyms = component_builder.create_component("ner_synonyms", _config)
message = Message("He was 6 feet away")
duckling.process(message)
synonyms.process(message) # checks that the synonym processor can handle entities that have int values
assert message is not None
def test_handles_pipeline_with_non_existing_component(component_builder):
_config = utilities.base_test_conf("spacy_sklearn")
_config.pipeline.append({"name": "my_made_up_component"})
with pytest.raises(Exception) as execinfo:
train.do_train(
_config,
data=DEFAULT_DATA_PATH,
component_builder=component_builder)
assert "Failed to find component" in str(execinfo.value)
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") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_load_and_persist_without_train(component_builder):
_config = utilities.base_test_conf("all_components")
trainer = Trainer(_config, component_builder)
persistor = create_persistor(_config)
persisted_path = trainer.persist(_config['path'], persistor,
project_name=_config['project'])
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_load_and_persist_without_train(component_builder, tmpdir):
_config = utilities.base_test_conf("all_components")
trainer = Trainer(_config, component_builder)
persistor = create_persistor(_config)
persisted_path = trainer.persist(tmpdir.strpath, persistor,
project_name="my_project")
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(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_model_noents(component_builder, tmpdir):
_config = utilities.base_test_conf("all_components")
(trained, _, persisted_path) = train.do_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_named_model(component_builder, tmpdir):
_config = utilities.base_test_conf("keyword")
(trained, _, persisted_path) = train.do_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(pipeline_template, component_builder, tmpdir):
_config = utilities.base_test_conf(pipeline_template)
(trained, _, persisted_path) = train.do_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_duckling_entity_extractor(component_builder):
_config = utilities.base_test_conf("all_components")
_config["duckling_dimensions"] = ["time"]
duckling = component_builder.create_component("ner_duckling", _config)
message = Message("Today is the 5th of May. Let us meet tomorrow.")
duckling.process(message)
entities = message.get("entities")
assert len(entities) == 3
# Test duckling with a defined date
message = Message("Let us meet tomorrow.", time="1381536182000") # 1381536182000 == 2013/10/12 02:03:02
duckling.process(message)
entities = message.get("entities")
assert len(entities) == 1
assert entities[0]["text"] == "tomorrow"
assert entities[0]["value"] == "2013-10-13T00:00:00.000Z"
def test_unintentional_synonyms_capitalized(component_builder):
_config = utilities.base_test_conf("all_components")
ner_syn = component_builder.create_component("ner_synonyms", _config)
examples = [
Message("Any Mexican restaurant will do", {
"intent": "restaurant_search",
"entities": [{"start": 4, "end": 11, "value": "Mexican", "entity": "cuisine"}]
}),
Message("I want Tacos!", {
"intent": "restaurant_search",
"entities": [{"start": 7, "end": 12, "value": "Mexican", "entity": "cuisine"}]
})
]
ner_syn.train(TrainingData(training_examples=examples), _config)
assert ner_syn.synonyms.get("mexican") is None
assert ner_syn.synonyms.get("tacos") == "Mexican"
def test_duckling_entity_extractor(component_builder):
_config = utilities.base_test_conf("all_components")
_config["duckling_dimensions"] = ["time"]
duckling = component_builder.create_component("ner_duckling", _config)
message = Message("Today is the 5th of May. Let us meet tomorrow.")
duckling.process(message)
entities = message.get("entities")
assert len(entities) == 3
# Test duckling with a defined date
message = Message("Let us meet tomorrow.", time="1381536182000") # 1381536182000 == 2013/10/12 02:03:02
duckling.process(message)
entities = message.get("entities")
assert len(entities) == 1
assert entities[0]["text"] == "tomorrow"
assert entities[0]["value"] == "2013-10-13T00:00:00.000Z"
def test_unintentional_synonyms_capitalized(component_builder):
_config = utilities.base_test_conf("all_components")
ner_syn = component_builder.create_component("ner_synonyms", _config)
examples = [
Message("Any Mexican restaurant will do", {
"intent": "restaurant_search",
"entities": [{"start": 4, "end": 11, "value": "Mexican", "entity": "cuisine"}]
}),
Message("I want Tacos!", {
"intent": "restaurant_search",
"entities": [{"start": 7, "end": 12, "value": "Mexican", "entity": "cuisine"}]
})
]
ner_syn.train(TrainingData(training_examples=examples), _config)
assert ner_syn.synonyms.get("mexican") is None
assert ner_syn.synonyms.get("tacos") == "Mexican"
def test_interpreter(pipeline_template, component_builder):
test_data = "data/examples/rasa/demo-rasa.json"
_conf = utilities.base_test_conf(pipeline_template)
_conf["data"] = test_data
td = training_data.load_data(test_data)
interpreter = utilities.interpreter_for(component_builder, _conf)
texts = ["good bye", "i am looking for an indian spot"]
for text in texts:
result = interpreter.parse(text, time=None)
assert result['text'] == text
assert not result['intent']['name'] or result['intent']['name'] in td.intents
assert result['intent']['confidence'] >= 0
# Ensure the model doesn't detect entity types that are not present
# Models on our test data set are not stable enough to require the exact entities to be found
for entity in result['entities']:
assert entity['entity'] in td.entities
def test_samples(pipeline_template, component_builder):
_conf = utilities.base_test_conf(pipeline_template)
_conf["data"] = "./data/examples/rasa/demo-rasa.json"
interpreter = utilities.interpreter_for(component_builder, _conf)
available_intents = [
"greet", "restaurant_search", "affirm", "goodbye", "None"
]
samples = [("good bye", {
'intent': 'goodbye',
'entities': []
}),
("i am looking for an indian spot", {
'intent':
'restaurant_search',
'entities': [{
"start": 20,
"end": 26,
"value": "indian",
"entity": "cuisine"
}, {
"end": 26,
"entity": "NORP",
"start": 20,
"value": "indian"
}]
})]
for text, gold in samples:
result = interpreter.parse(text, time=None)
assert result['text'] == text, \
"Wrong text for sample '{}'".format(text)
assert result['intent']['name'] in available_intents, \
"Wrong intent for sample '{}'".format(text)
assert result['intent']['confidence'] >= 0, \
"Low confidence for sample '{}'".format(text)
# This ensures the model doesn't detect entities that are not present
# Models on our test data set are not stable enough to require the entities to be found
for entity in result['entities']:
del entity["extractor"]
assert entity in gold['entities'], \
"Wrong entities for sample '{}'".format(text)
def test_samples(pipeline_template, component_builder):
_conf = utilities.base_test_conf(pipeline_template)
_conf["data"] = "./data/examples/rasa/demo-rasa.json"
interpreter = utilities.interpreter_for(component_builder, _conf)
available_intents = ["greet", "restaurant_search", "affirm", "goodbye", "None"]
samples = [
(
"good bye",
{
'intent': 'goodbye',
'entities': []
}
),
(
"i am looking for an indian spot",
{
'intent': 'restaurant_search',
'entities': [{"start": 20, "end": 26, "value": "indian", "entity": "cuisine"}]
}
)
]
for text, gold in samples:
result = interpreter.parse(text, time=None)
assert result['text'] == text, \
"Wrong text for sample '{}'".format(text)
assert result['intent']['name'] in available_intents, \
"Wrong intent for sample '{}'".format(text)
assert result['intent']['confidence'] >= 0, \
"Low confidence for sample '{}'".format(text)
# This ensures the model doesn't detect entities that are not present
# Models on our test data set are not stable enough to require the entities to be found
for entity in result['entities']:
del entity["extractor"]
assert entity in gold['entities'], \
"Wrong entities for sample '{}'".format(text)
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
def duckling_interpreter(component_builder):
_conf = utilities.base_test_conf("")
_conf["pipeline"] = ["ner_duckling"]
_conf["data"] = "./data/examples/rasa/demo-rasa.json"
return utilities.interpreter_for(component_builder, _conf)
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_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 duckling_interpreter(component_builder):
_conf = utilities.base_test_conf("")
_conf["pipeline"] = ["ner_duckling"]
_conf["data"] = "./data/examples/rasa/demo-rasa.json"
return utilities.interpreter_for(component_builder, _conf)
