Policies¶

Configuring Policies¶

The rasa.core.policies.Policy class decides which action to take at every step in the conversation.

There are different policies to choose from, and you can include multiple policies in a single rasa.core.agent.Agent.

Your project’s config.yml file takes a policies key which you can use to customize the policies your assistant uses. In the example below, the last two lines show how to use a custom policy class and pass arguments to it.

policies:
  - name: "TEDPolicy"
    featurizer:
    - name: MaxHistoryTrackerFeaturizer
      max_history: 5
      state_featurizer:
        - name: BinarySingleStateFeaturizer
  - name: "MemoizationPolicy"
    max_history: 5
  - name: "FallbackPolicy"
    nlu_threshold: 0.4
    core_threshold: 0.3
    fallback_action_name: "my_fallback_action"
  - name: "path.to.your.policy.class"
    arg1: "..."

* out_of_scope
   - utter_default
* out_of_scope
   - utter_default
* out_of_scope
   - utter_help_message

# thanks
* thankyou
   - utter_youarewelcome

# bye
* goodbye
   - utter_goodbye

Action Selection¶

At every turn, each policy defined in your configuration will predict a next action with a certain confidence level. For more information about how each policy makes its decision, read into the policy’s description below. The bot’s next action is then decided by the policy that predicts with the highest confidence.

In the case that two policies predict with equal confidence (for example, the Memoization and Mapping Policies always predict with confidence of either 0 or 1), the priority of the policies is considered. Rasa policies have default priorities that are set to ensure the expected outcome in the case of a tie. They look like this, where higher numbers have higher priority:

5. FormPolicy

4. FallbackPolicy and TwoStageFallbackPolicy

3. MemoizationPolicy and AugmentedMemoizationPolicy

2. MappingPolicy

1. TEDPolicy and SklearnPolicy

This priority hierarchy ensures that, for example, if there is an intent with a mapped action, but the NLU confidence is not above the nlu_threshold, the bot will still fall back. In general, it is not recommended to have more than one policy per priority level, and some policies on the same priority level, such as the two fallback policies, strictly cannot be used in tandem.

If you create your own policy, use these priorities as a guide for figuring out the priority of your policy. If your policy is a machine learning policy, it should most likely have priority 1, the same as the Rasa machine learning policies.

Embedding Policy¶

Warning

EmbeddingPolicy was renamed to TEDPolicy. Please use TED Policy instead of EmbeddingPolicy in your policy configuration. The functionality of the policy stayed the same.

TED Policy¶

The Transformer Embedding Dialogue (TED) Policy is described in our paper.

This policy has a pre-defined architecture, which comprises 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 idea.

It is recommended to use state_featurizer=LabelTokenizerSingleStateFeaturizer(...) (see Featurization of Conversations for details).

Configuration:

Configuration parameters can be passed as parameters to the TEDPolicy within the configuration file. If you want to adapt your model, start by modifying the following parameters:

• epochs: This parameter sets the number of times the algorithm will see the training data (default: 1). One epoch is equals to one forward pass and one backward pass of all the training examples. Sometimes the model needs more epochs to properly learn. Sometimes more epochs don’t influence the performance. The lower the number of epochs the faster the model is trained.

• hidden_layers_sizes: This parameter allows you to define the number of feed forward layers and their output dimensions for dialogues and intents (default: dialogue: [], label: []). Every entry in the list corresponds to a feed forward layer. For example, if you set dialogue: [256, 128], we will add two feed forward layers in front of the transformer. The vectors of the input tokens (coming from the dialogue) will be passed on to those layers. The first layer will have an output dimension of 256 and the second layer will have an output dimension of 128. If an empty list is used (default behavior), no feed forward layer will be added. Make sure to use only positive integer values. Usually, numbers of power of two are used. Also, it is usual practice to have decreasing values in the list: next value is smaller or equal to the value before.

• number_of_transformer_layers: This parameter sets the number of transformer layers to use (default: 1). The number of transformer layers corresponds to the transformer blocks to use for the model.

• transformer_size: This parameter sets the number of units in the transformer (default: 128). The vectors coming out of the transformers will have the given transformer_size.

• weight_sparsity: This parameter defines the fraction of kernel weights that are set to 0 for all feed forward layers in the model (default: 0.8). The value should be between 0 and 1. If you set weight_sparsity to 0, no kernel weights will be set to 0, the layer acts as a standard feed forward layer. You should not set weight_sparsity to 1 as this would result in all kernel weights being 0, i.e. the model is not able to learn.

Warning

Pass an appropriate number, for example 50, of epochs to the TEDPolicy, otherwise the policy will be trained only for 1 epoch.

Warning

Default max_history for this policy is None which means it’ll use the FullDialogueTrackerFeaturizer. We recommend to set max_history to some finite value in order to use MaxHistoryTrackerFeaturizer for faster training. See Featurization of Conversations for details. We recommend to increase batch_size for MaxHistoryTrackerFeaturizer (e.g. "batch_size": [32, 64])

The above configuration parameters are the ones you should configure to fit your model to your data. However, additional parameters exist that can be adapted.

+---------------------------------+------------------+--------------------------------------------------------------+
| Parameter                       | Default Value    | Description                                                  |
+=================================+==================+==============================================================+
| hidden_layers_sizes             | dialogue: []     | Hidden layer sizes for layers before the embedding layers    |
|                                 | label: []        | for dialogue and labels. The number of hidden layers is      |
|                                 |                  | equal to the length of the corresponding.                    |
+---------------------------------+------------------+--------------------------------------------------------------+
| transformer_size                | 128              | Number of units in transformer.                              |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_transformer_layers    | 1                | Number of transformer layers.                                |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_attention_heads       | 4                | Number of attention heads in transformer.                    |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_key_relative_attention      | False            | If 'True' use key relative embeddings in attention.          |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_value_relative_attention    | False            | If 'True' use value relative embeddings in attention.        |
+---------------------------------+------------------+--------------------------------------------------------------+
| max_relative_position           | None             | Maximum position for relative embeddings.                    |
+---------------------------------+------------------+--------------------------------------------------------------+
| batch_size                      | [8, 32]          | Initial and final value for batch sizes.                     |
|                                 |                  | Batch size will be linearly increased for each epoch.        |
+---------------------------------+------------------+--------------------------------------------------------------+
| batch_strategy                  | "balanced"       | Strategy used when creating batches.                         |
|                                 |                  | Can be either 'sequence' or 'balanced'.                      |
+---------------------------------+------------------+--------------------------------------------------------------+
| epochs                          | 1                | Number of epochs to train.                                   |
+---------------------------------+------------------+--------------------------------------------------------------+
| random_seed                     | None             | Set random seed to any 'int' to get reproducible results.    |
+---------------------------------+------------------+--------------------------------------------------------------+
| embedding_dimension             | 20               | Dimension size of embedding vectors.                         |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_negative_examples     | 20               | The number of incorrect labels. The algorithm will minimize  |
|                                 |                  | their similarity to the user input during training.          |
+---------------------------------+------------------+--------------------------------------------------------------+
| similarity_type                 | "auto"           | Type of similarity measure to use, either 'auto' or 'cosine' |
|                                 |                  | or 'inner'.                                                  |
+---------------------------------+------------------+--------------------------------------------------------------+
| loss_type                       | "softmax"        | The type of the loss function, either 'softmax' or 'margin'. |
+---------------------------------+------------------+--------------------------------------------------------------+
| ranking_length                  | 10               | Number of top actions to normalize scores for loss type      |
|                                 |                  | 'softmax'. Set to 0 to turn off normalization.               |
+---------------------------------+------------------+--------------------------------------------------------------+
| maximum_positive_similarity     | 0.8              | Indicates how similar the algorithm should try to make       |
|                                 |                  | embedding vectors for correct labels.                        |
|                                 |                  | Should be 0.0 < ... < 1.0 for 'cosine' similarity type.      |
+---------------------------------+------------------+--------------------------------------------------------------+
| maximum_negative_similarity     | -0.2             | Maximum negative similarity for incorrect labels.            |
|                                 |                  | Should be -1.0 < ... < 1.0 for 'cosine' similarity type.     |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_maximum_negative_similarity | True             | If 'True' the algorithm only minimizes maximum similarity    |
|                                 |                  | over incorrect intent labels, used only if 'loss_type' is    |
|                                 |                  | set to 'margin'.                                             |
+---------------------------------+------------------+--------------------------------------------------------------+
| scale_loss                      | True             | Scale loss inverse proportionally to confidence of correct   |
|                                 |                  | prediction.                                                  |
+---------------------------------+------------------+--------------------------------------------------------------+
| regularization_constant         | 0.001            | The scale of regularization.                                 |
+---------------------------------+------------------+--------------------------------------------------------------+
| negative_margin_scale           | 0.8              | The scale of how important it is to minimize the maximum     |
|                                 |                  | similarity between embeddings of different labels.           |
+---------------------------------+------------------+--------------------------------------------------------------+
| drop_rate_dialogue              | 0.1              | Dropout rate for embedding layers of dialogue features.      |
|                                 |                  | Value should be between 0 and 1.                             |
|                                 |                  | The higher the value the higher the regularization effect.   |
+---------------------------------+------------------+--------------------------------------------------------------+
| drop_rate_label                 | 0.0              | Dropout rate for embedding layers of label features.         |
|                                 |                  | Value should be between 0 and 1.                             |
|                                 |                  | The higher the value the higher the regularization effect.   |
+---------------------------------+------------------+--------------------------------------------------------------+
| drop_rate_attention             | 0.0              | Dropout rate for attention. Value should be between 0 and 1. |
|                                 |                  | The higher the value the higher the regularization effect.   |
+---------------------------------+------------------+--------------------------------------------------------------+
| weight_sparsity                 | 0.8              | Sparsity of the weights in dense layers.                     |
|                                 |                  | Value should be between 0 and 1.                             |
+---------------------------------+------------------+--------------------------------------------------------------+
| evaluate_every_number_of_epochs | 20               | How often to calculate validation accuracy.                  |
|                                 |                  | Set to '-1' to evaluate just once at the end of training.    |
+---------------------------------+------------------+--------------------------------------------------------------+
| evaluate_on_number_of_examples  | 0                | How many examples to use for hold out validation set.        |
|                                 |                  | Large values may hurt performance, e.g. model accuracy.      |
+---------------------------------+------------------+--------------------------------------------------------------+
| tensorboard_log_directory       | None             | If you want to use tensorboard to visualize training         |
|                                 |                  | metrics, set this option to a valid output directory. You    |
|                                 |                  | can view the training metrics after training in tensorboard  |
|                                 |                  | via 'tensorboard --logdir <path-to-given-directory>'.        |
+---------------------------------+------------------+--------------------------------------------------------------+
| tensorboard_log_level           | "epoch"          | Define when training metrics for tensorboard should be       |
|                                 |                  | logged. Either after every epoch ('epoch') or for every      |
|                                 |                  | training step ('minibatch').                                 |
+---------------------------------+------------------+--------------------------------------------------------------+

Warning

If evaluate_on_number_of_examples is non zero, random examples will be picked by stratified split and used as hold out validation set, so they will be excluded from training data. We suggest to set it to zero if data set contains a lot of unique examples of dialogue turns.

Note

For cosine similarity maximum_positive_similarity and maximum_negative_similarity should be between -1 and 1.

Note

There is an option to use linearly increasing batch size. The idea comes from https://arxiv.org/abs/1711.00489. In order to do it pass a list to batch_size, e.g. "batch_size": [8, 32] (default behavior). If constant batch_size is required, pass an int, e.g. "batch_size": 8.

Note

The parameter maximum_negative_similarity is set to a negative value to mimic the original starspace algorithm in the case maximum_negative_similarity = maximum_positive_similarity and use_maximum_negative_similarity = False. See starspace paper for details.

Mapping Policy¶

The MappingPolicy can be used to directly map intents to actions. The mappings are assigned by giving an intent the property triggers, e.g.:

intents:
 - ask_is_bot:
     triggers: action_is_bot

An intent can only be mapped to at most one action. The bot will run the mapped action once it receives a message of the triggering intent. Afterwards, it will listen for the next message. With the next user message, normal prediction will resume.

If you do not want your intent-action mapping to affect the dialogue history, the mapped action must return a UserUtteranceReverted() event. This will delete the user’s latest message, along with any events that happened after it, from the dialogue history. This means you should not include the intent-action interaction in your stories.

For example, if a user asks “Are you a bot?” off-topic in the middle of the flow, you probably want to answer without that interaction affecting the next action prediction. A triggered custom action can do anything, but here’s a simple example that dispatches a bot utterance and then reverts the interaction:

class ActionIsBot(Action):
"""Revertible mapped action for utter_is_bot"""

def name(self):
    return "action_is_bot"

def run(self, dispatcher, tracker, domain):
    dispatcher.utter_template(template="utter_is_bot")
    return [UserUtteranceReverted()]

Memoization Policy¶

The MemoizationPolicy just memorizes the conversations in your training data. It predicts the next action with confidence 1.0 if this exact conversation exists in the training data, otherwise it predicts None with confidence 0.0.

Augmented Memoization Policy¶

The AugmentedMemoizationPolicy remembers examples from training stories for up to max_history turns, just like the MemoizationPolicy. Additionally, it has a forgetting mechanism that will forget a certain amount of steps in the conversation history and try to find a match in your stories with the reduced history. It predicts the next action with confidence 1.0 if a match is found, otherwise it predicts None with confidence 0.0.

Fallback Policy¶

The FallbackPolicy invokes a fallback action if at least one of the following occurs:

1. The intent recognition has a confidence below nlu_threshold.

2. The highest ranked intent differs in confidence with the second highest ranked intent by less than ambiguity_threshold.

3. None of the dialogue policies predict an action with confidence higher than core_threshold.

Configuration:

The thresholds and fallback action can be adjusted in the policy configuration file as parameters of the FallbackPolicy:

policies:
  - name: "FallbackPolicy"
    nlu_threshold: 0.3
    ambiguity_threshold: 0.1
    core_threshold: 0.3
    fallback_action_name: 'action_default_fallback'

nlu_threshold	Min confidence needed to accept an NLU prediction
ambiguity_threshold	Min amount by which the confidence of the top intent must exceed that of the second highest ranked intent.
core_threshold	Min confidence needed to accept an action prediction from Rasa Core
fallback_action_name	Name of the fallback action to be called if the confidence of intent or action is below the respective threshold

You can also configure the FallbackPolicy in your python code:

from rasa.core.policies.fallback import FallbackPolicy
from rasa.core.policies.keras_policy import TEDPolicy
from rasa.core.agent import Agent

fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
                          core_threshold=0.3,
                          nlu_threshold=0.3,
                          ambiguity_threshold=0.1)

agent = Agent("domain.yml", policies=[TEDPolicy(), fallback])

Note

You can include either the FallbackPolicy or the TwoStageFallbackPolicy in your configuration, but not both.

Two-Stage Fallback Policy¶

The TwoStageFallbackPolicy handles low NLU confidence in multiple stages by trying to disambiguate the user input.

• If an NLU prediction has a low confidence score or is not significantly higher than the second highest ranked prediction, the user is asked to affirm the classification of the intent.

  • If they affirm, the story continues as if the intent was classified with high confidence from the beginning.

  • If they deny, the user is asked to rephrase their message.

• Rephrasing

  • If the classification of the rephrased intent was confident, the story continues as if the user had this intent from the beginning.

  • If the rephrased intent was not classified with high confidence, the user is asked to affirm the classified intent.

• Second affirmation

  • If the user affirms the intent, the story continues as if the user had this intent from the beginning.

  • If the user denies, the original intent is classified as the specified deny_suggestion_intent_name, and an ultimate fallback action is triggered (e.g. a handoff to a human).

Configuration:

To use the TwoStageFallbackPolicy, include the following in your policy configuration.

policies:
  - name: TwoStageFallbackPolicy
    nlu_threshold: 0.3
    ambiguity_threshold: 0.1
    core_threshold: 0.3
    fallback_core_action_name: "action_default_fallback"
    fallback_nlu_action_name: "action_default_fallback"
    deny_suggestion_intent_name: "out_of_scope"

nlu_threshold	Min confidence needed to accept an NLU prediction
ambiguity_threshold	Min amount by which the confidence of the top intent must exceed that of the second highest ranked intent.
core_threshold	Min confidence needed to accept an action prediction from Rasa Core
fallback_core_action_name	Name of the fallback action to be called if the confidence of Rasa Core action prediction is below the core_threshold. This action is to propose the recognized intents
fallback_nlu_action_name	Name of the fallback action to be called if the confidence of Rasa NLU intent classification is below the nlu_threshold. This action is called when the user denies the second time
deny_suggestion_intent_name	The name of the intent which is used to detect that the user denies the suggested intents

Note

You can include either the FallbackPolicy or the TwoStageFallbackPolicy in your configuration, but not both.

Form Policy¶

The FormPolicy is an extension of the MemoizationPolicy which handles the filling of forms. Once a FormAction is called, the FormPolicy will continually predict the FormAction until all required slots in the form are filled. For more information, see Forms.