rasa.core.policies.sklearn_policy

SklearnPolicy Objects

classSklearnPolicy(Policy)

Use an sklearn classifier to train a policy.

__init__

__init__(featurizer: Optional[MaxHistoryTrackerFeaturizer]=None, priority:int= DEFAULT_POLICY_PRIORITY, max_history:int= DEFAULT_MAX_HISTORY, model: Optional["sklearn.base.BaseEstimator"]=None, param_grid: Optional[Union[Dict[Text, List], List[Dict]]]=None, cv: Optional[int]=None, scoring: Optional[Union[Text, List, Dict, Callable]]="accuracy", label_encoder: LabelEncoder = LabelEncoder(), shuffle:bool=True, zero_state_features: Optional[Dict[Text, List["Features"]]]=None, **kwargs: Any) -> None

Create a new sklearn policy.

Arguments:

• featurizer - Featurizer used to convert the training data into vector format.
• priority - Policy priority
• max_history - Maximum history of the dialogs.
• model - The sklearn model or model pipeline.
• param_grid - If param_grid is not None and cv is given, a grid search on the given param_grid is performed (e.g. param_grid={'n_estimators': [50, 100]}).
• cv - If cv is not None, perform a cross validation on the training data. cv should then conform to the sklearn standard (e.g. cv=5 for a 5-fold cross-validation).
• scoring - Scoring strategy, using the sklearn standard.
• label_encoder - Encoder for the labels. Must implement an _inverse_transform_ method.
• shuffle - Whether to shuffle training data.
• zero_state_features - Contains default feature values for attributes.

model_architecture

model_architecture(**kwargs: Any) -> Any

Sets model parameters for training.

predict_action_probabilities

predict_action_probabilities(tracker: DialogueStateTracker, domain: Domain, interpreter: NaturalLanguageInterpreter, **kwargs: Any) -> PolicyPrediction

Predicts the next action the bot should take after seeing the tracker.

Arguments:

• tracker - the rasa.core.trackers.DialogueStateTracker
• domain - the rasa.shared.core.domain.Domain
• interpreter - Interpreter which may be used by the policies to create additional features.

Returns:

The policy's prediction (e.g. the probabilities for the actions).

persist

persist(path: Union[Text, Path]) -> None

Persists the policy properties (see parent class for more information).

load

@classmethod
load(cls, path: Union[Text, Path], should_finetune: bool=False, **kwargs: Any) -> Policy

See the docstring for Policy.load.