async def test_sparse_feature_sizes_decreased_incremental_training(
iter1_path: Text,
iter2_path: Text,
should_raise_exception: bool,
create_response_selector: Callable[
[Dict[Text, Any]], ResponseSelectorGraphComponent
],
load_response_selector: Callable[[Dict[Text, Any]], ResponseSelectorGraphComponent],
default_execution_context: ExecutionContext,
train_and_preprocess: Callable[..., Tuple[TrainingData, List[GraphComponent]]],
process_message: Callable[..., Message],
):
pipeline = [
{"component": WhitespaceTokenizerGraphComponent},
{"component": LexicalSyntacticFeaturizerGraphComponent},
{"component": RegexFeaturizerGraphComponent},
{"component": CountVectorsFeaturizerGraphComponent},
{
"component": CountVectorsFeaturizerGraphComponent,
"analyzer": "char_wb",
"min_ngram": 1,
"max_ngram": 4,
},
]
training_data, loaded_pipeline = train_and_preprocess(pipeline, iter1_path)
response_selector = create_response_selector({EPOCHS: 1})
response_selector.train(training_data=training_data)
message = Message(data={TEXT: "Rasa is great!"})
message = process_message(loaded_pipeline, message)
message2 = copy.deepcopy(message)
classified_message = response_selector.process([message])[0]
default_execution_context.is_finetuning = True
loaded_selector = load_response_selector({EPOCHS: 1})
classified_message2 = loaded_selector.process([message2])[0]
assert classified_message2.fingerprint() == classified_message.fingerprint()
if should_raise_exception:
with pytest.raises(Exception) as exec_info:
training_data2, loaded_pipeline2 = train_and_preprocess(
pipeline, iter2_path
)
loaded_selector.train(training_data=training_data2)
assert "Sparse feature sizes have decreased" in str(exec_info.value)
else:
training_data2, loaded_pipeline2 = train_and_preprocess(pipeline, iter2_path)
loaded_selector.train(training_data=training_data2)
assert loaded_selector.model
def test_epoch_override_when_loaded(
self,
trained_policy: TEDPolicy,
should_finetune: bool,
epoch_override: int,
expected_epoch_value: int,
resource: Resource,
model_storage: ModelStorage,
execution_context: ExecutionContext,
):
execution_context.is_finetuning = should_finetune
loaded_policy = trained_policy.__class__.load(
{
**self._config(), EPOCH_OVERRIDE: epoch_override
},
model_storage,
resource,
execution_context,
)
assert loaded_policy.config[EPOCHS] == expected_epoch_value
