Version: 3.x

rasa.core.policies.sklearn_policy

SklearnPolicy Objects

class SklearnPolicy(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 :class:rasa.core.trackers.DialogueStateTracker
  • domain - the :class: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.