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Version: Master/Unreleased

rasa.core.policies.ted_policy

TEDPolicyGraphComponent Objects

class TEDPolicyGraphComponent(PolicyGraphComponent)

Transformer Embedding Dialogue (TED) Policy.

The model architecture is described in detail in https://arxiv.org/abs/1910.00486. In summary, the architecture comprises of the following steps:

- concatenate user input (user intent and entities), previous system actions,
slots and active forms for each time step into an input vector to
pre-transformer embedding layer;
- feed it to transformer;
- apply a dense layer to the output of the transformer to get embeddings of a
dialogue for each time step;
- apply a dense layer to create embeddings for system actions for each time
step;
- calculate the similarity between the dialogue embedding and embedded system
actions. This step is based on the StarSpace
(https://arxiv.org/abs/1709.03856) idea.

get_default_config

@staticmethod
def get_default_config() -> Dict[Text, Any]

Returns the default config (see parent class for full docstring).

__init__

def __init__(config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, model: Optional[RasaModel] = None, featurizer: Optional[TrackerFeaturizer] = None, fake_features: Optional[Dict[Text, List[Features]]] = None, entity_tag_specs: Optional[List[EntityTagSpec]] = None) -> None

Declares instance variables with default values.

model_class

@staticmethod
def model_class() -> Type[TED]

Gets the class of the model architecture to be used by the policy.

Returns:

Required class.

run_training

def run_training(model_data: RasaModelData, label_ids: Optional[np.ndarray] = None) -> None

Feeds the featurized training data to the model.

Arguments:

  • model_data - Featurized training data.
  • label_ids - Label ids corresponding to the data points in model_data. These may or may not be used by the function depending on how the policy is trained.

train

def train(training_trackers: List[TrackerWithCachedStates], domain: Domain, precomputations: Optional[MessageContainerForCoreFeaturization] = None) -> Resource

Trains the policy (see parent class for full docstring).

predict_action_probabilities

def predict_action_probabilities(tracker: DialogueStateTracker, domain: Domain, precomputations: Optional[MessageContainerForCoreFeaturization] = None, rule_only_data: Optional[Dict[Text, Any]] = None, **kwargs: Any, ,) -> PolicyPrediction

Predicts the next action (see parent class for full docstring).

persist

def persist() -> None

Persists the policy to a storage.

persist_model_utilities

def persist_model_utilities(model_path: Path) -> None

Persists model's utility attributes like model weights, etc.

Arguments:

  • model_path - Path where model is to be persisted

load

@classmethod
def load(cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any, ,) -> TEDPolicyGraphComponent

Loads a policy from the storage (see parent class for full docstring).

TED Objects

class TED(TransformerRasaModel)

TED model architecture from https://arxiv.org/abs/1910.00486.

__init__

def __init__(data_signature: Dict[Text, Dict[Text, List[FeatureSignature]]], config: Dict[Text, Any], max_history_featurizer_is_used: bool, label_data: RasaModelData, entity_tag_specs: Optional[List[EntityTagSpec]]) -> None

Initializes the TED model.

Arguments:

  • data_signature - the data signature of the input data
  • config - the model configuration
  • max_history_featurizer_is_used - if 'True' only the last dialogue turn will be used
  • label_data - the label data
  • entity_tag_specs - the entity tag specifications

batch_loss

def batch_loss(batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]]) -> tf.Tensor

Calculates the loss for the given batch.

Arguments:

  • batch_in - The batch.

Returns:

The loss of the given batch.

prepare_for_predict

def prepare_for_predict() -> None

Prepares the model for prediction.

batch_predict

def batch_predict(batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]]) -> Dict[Text, Union[tf.Tensor, Dict[Text, tf.Tensor]]]

Predicts the output of the given batch.

Arguments:

  • batch_in - The batch.

Returns:

The output to predict.