def test_fitting_should_be_reproducible_after_serialization(self):
# Given
dataset = BEVERAGE_DATASET
validated_dataset = validate_and_format_dataset(dataset)
seed1 = 666
seed2 = 42
config = ProbabilisticIntentParserConfig(
intent_classifier_config=LogRegIntentClassifierConfig(
random_seed=seed1),
slot_filler_config=CRFSlotFillerConfig(random_seed=seed2))
parser = ProbabilisticIntentParser(config)
parser.persist(self.tmp_file_path)
# When
fitted_parser_1 = ProbabilisticIntentParser.from_path(
self.tmp_file_path).fit(validated_dataset)
fitted_parser_2 = ProbabilisticIntentParser.from_path(
self.tmp_file_path).fit(validated_dataset)
# Then
feature_weights_1 = fitted_parser_1.slot_fillers[
"MakeTea"].crf_model.state_features_
feature_weights_2 = fitted_parser_2.slot_fillers[
"MakeTea"].crf_model.state_features_
self.assertEqual(feature_weights_1, feature_weights_2)
def test_fitting_should_be_reproducible_after_serialization(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: MakeTea
utterances:
- make me a [beverage_temperature:Temperature](hot) cup of tea
- make me [number_of_cups:snips/number](five) tea cups
---
type: intent
name: MakeCoffee
utterances:
- make me [number_of_cups:snips/number](one) cup of coffee please
- brew [number_of_cups] cups of coffee""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
seed1 = 666
seed2 = 42
config = ProbabilisticIntentParserConfig(
intent_classifier_config=LogRegIntentClassifierConfig(
random_seed=seed1),
slot_filler_config=CRFSlotFillerConfig(random_seed=seed2))
shared = self.get_shared_data(dataset)
parser = ProbabilisticIntentParser(config, **shared)
parser.persist(self.tmp_file_path)
# When
fitted_parser_1 = ProbabilisticIntentParser.from_path(
self.tmp_file_path, **shared).fit(dataset)
fitted_parser_2 = ProbabilisticIntentParser.from_path(
self.tmp_file_path, **shared).fit(dataset)
# Then
feature_weights_1 = fitted_parser_1.slot_fillers[
"MakeTea"].crf_model.state_features_
feature_weights_2 = fitted_parser_2.slot_fillers[
"MakeTea"].crf_model.state_features_
self.assertEqual(feature_weights_1, feature_weights_2)
def test_should_be_deserializable(self):
# When
register_processing_unit(TestIntentClassifier)
register_processing_unit(TestSlotFiller)
config = ProbabilisticIntentParserConfig(
intent_classifier_config=TestIntentClassifierConfig(),
slot_filler_config=TestSlotFillerConfig()
)
parser_dict = {
"unit_name": "probabilistic_intent_parser",
"slot_fillers": [
{
"intent": "MakeCoffee",
"slot_filler_name": "slot_filler_MakeCoffee"
},
{
"intent": "MakeTea",
"slot_filler_name": "slot_filler_MakeTea"
}
],
"config": config.to_dict(),
}
self.tmp_file_path.mkdir()
(self.tmp_file_path / "intent_classifier").mkdir()
(self.tmp_file_path / "slot_filler_MakeCoffee").mkdir()
(self.tmp_file_path / "slot_filler_MakeTea").mkdir()
self.writeJsonContent(self.tmp_file_path / "intent_parser.json",
parser_dict)
self.writeJsonContent(
self.tmp_file_path / "intent_classifier" / "metadata.json",
{"unit_name": "test_intent_classifier"})
self.writeJsonContent(
self.tmp_file_path / "slot_filler_MakeCoffee" / "metadata.json",
{"unit_name": "test_slot_filler"})
self.writeJsonContent(
self.tmp_file_path / "slot_filler_MakeTea" / "metadata.json",
{"unit_name": "test_slot_filler"})
# When
parser = ProbabilisticIntentParser.from_path(self.tmp_file_path)
# Then
self.assertDictEqual(parser.config.to_dict(), config.to_dict())
self.assertIsNotNone(parser.intent_classifier)
self.assertListEqual(sorted(parser.slot_fillers),
["MakeCoffee", "MakeTea"])
def test_should_be_deserializable_before_fitting(self):
# When
config = ProbabilisticIntentParserConfig().to_dict()
parser_dict = {
"unit_name": "probabilistic_intent_parser",
"config": config,
"intent_classifier": None,
"slot_fillers": dict(),
}
self.tmp_file_path.mkdir()
metadata = {"unit_name": "probabilistic_intent_parser"}
self.writeJsonContent(self.tmp_file_path / "metadata.json", metadata)
self.writeJsonContent(self.tmp_file_path / "intent_parser.json",
parser_dict)
# When
parser = ProbabilisticIntentParser.from_path(self.tmp_file_path)
# Then
self.assertEqual(parser.config.to_dict(), config)
self.assertIsNone(parser.intent_classifier)
self.assertDictEqual(dict(), parser.slot_fillers)
def test_should_be_deserializable(self):
# When
@IntentClassifier.register("my_intent_classifier", True)
class MyIntentClassifier(MockIntentClassifier):
pass
@SlotFiller.register("my_slot_filler", True)
class MySlotFiller(MockSlotFiller):
pass
parser_config = {
"unit_name": "probabilistic_intent_parser",
"intent_classifier_config": {
"unit_name": "my_intent_classifier"
},
"slot_filler_config": {
"unit_name": "my_slot_filler"
}
}
parser_dict = {
"unit_name":
"probabilistic_intent_parser",
"slot_fillers": [{
"intent": "MakeCoffee",
"slot_filler_name": "slot_filler_MakeCoffee"
}, {
"intent": "MakeTea",
"slot_filler_name": "slot_filler_MakeTea"
}],
"config":
parser_config,
}
self.tmp_file_path.mkdir()
(self.tmp_file_path / "intent_classifier").mkdir()
(self.tmp_file_path / "slot_filler_MakeCoffee").mkdir()
(self.tmp_file_path / "slot_filler_MakeTea").mkdir()
self.writeJsonContent(self.tmp_file_path / "intent_parser.json",
parser_dict)
self.writeJsonContent(
self.tmp_file_path / "intent_classifier" / "metadata.json", {
"unit_name": "my_intent_classifier",
"fitted": True
})
self.writeJsonContent(
self.tmp_file_path / "slot_filler_MakeCoffee" / "metadata.json", {
"unit_name": "my_slot_filler",
"fitted": True
})
self.writeJsonContent(
self.tmp_file_path / "slot_filler_MakeTea" / "metadata.json", {
"unit_name": "my_slot_filler",
"fitted": True
})
# When
parser = ProbabilisticIntentParser.from_path(self.tmp_file_path)
# Then
self.assertDictEqual(parser.config.to_dict(), parser_config)
self.assertIsInstance(parser.intent_classifier, MyIntentClassifier)
self.assertListEqual(sorted(parser.slot_fillers),
["MakeCoffee", "MakeTea"])
for slot_filler in itervalues(parser.slot_fillers):
self.assertIsInstance(slot_filler, MySlotFiller)