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Version: Main/Unreleased


UserMessageGenerator Objects

class UserMessageGenerator()


def message_for_data(structured_info: Dict[Text, Any]) -> Any

Find a data sample with the same intent.


def persist_graph(graph: "networkx.Graph", output_file: Text) -> None

Plots the graph and persists it into a html file.


def visualize_neighborhood(current: Optional[List[Event]],
event_sequences: List[List[Event]],
output_file: Optional[Text] = None,
max_history: int = 2,
nlu_training_data: Optional["TrainingData"] = None,
should_merge_nodes: bool = True,
max_distance: int = 1,
fontsize: int = 12) -> "networkx.MultiDiGraph"

Given a set of event lists, visualizing the flows.


def visualize_stories(story_steps: List[StoryStep],
domain: Domain,
output_file: Optional[Text],
max_history: int,
nlu_training_data: Optional["TrainingData"] = None,
should_merge_nodes: bool = True,
fontsize: int = 12) -> "networkx.MultiDiGraph"

Given a set of stories, generates a graph visualizing the flows in the stories.

Visualization is always a trade off between making the graph as small as possible while at the same time making sure the meaning doesn't change to "much". The algorithm will compress the graph generated from the stories to merge nodes that are similar. Hence, the algorithm might create paths through the graph that aren't actually specified in the stories, but we try to minimize that.

Output file defines if and where a file containing the plotted graph should be stored.

The history defines how much 'memory' the graph has. This influences in which situations the algorithm will merge nodes. Nodes will only be merged if they are equal within the history, this means the larger the history is we take into account the less likely it is we merge any nodes.

The training data parameter can be used to pass in a Rasa NLU training data instance. It will be used to replace the user messages from the story file with actual messages from the training data.