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## UserMessageGenerator Objects

### class UserMessageGenerator()

#### message_for_data

```python
| message_for_data(structured_info: Dict[Text, Any])-> Any
```

Find a data sample with the same intent and entities.

Given the parsed data from a message (intent and entities) finds a message in the data that has the same intent and entities.

#### persist_graph

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

Plots the graph and persists it into a html file.

#### visualize_neighborhood

```python
async visualize_neighborhood(current: Optional[List[Event]], event_sequences: List[List[Event]], output_file: Optional[Text]=None, max_history:int=2, interpreter: NaturalLanguageInterpreter = RegexInterpreter(), nlu_training_data: Optional["TrainingData"]=None, should_merge_nodes:bool=True, max_distance:int=1, fontsize:int=12)
```

Given a set of event lists, visualizing the flows.

#### visualize_stories

```python
async visualize_stories(story_steps: List[StoryStep], domain: Domain, output_file: Optional[Text], max_history:int, interpreter: NaturalLanguageInterpreter = RegexInterpreter(), nlu_training_data: Optional["TrainingData"]=None, should_merge_nodes:bool=True, fontsize:int=12)
```

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 too "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.