def test_story_visualization_with_training_data(domain: Domain, tmp_path: Path,
nlu_data_path: Text):
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
out_file = tmp_path / "graph.html"
test_text = "test text"
test_intent = "affirm"
generated_graph = visualization.visualize_stories(
story_steps,
domain,
output_file=str(out_file),
max_history=3,
should_merge_nodes=False,
nlu_training_data=TrainingData(
[Message({
TEXT: test_text,
INTENT: test_intent
})]),
)
assert test_text in out_file.read_text()
assert test_intent not in out_file.read_text()
assert len(generated_graph.nodes()) == 51
assert len(generated_graph.edges()) == 56
async def visualize(
self,
resource_name: Text,
output_file: Text,
max_history: Optional[int] = None,
nlu_training_data: Optional[TrainingData] = None,
should_merge_nodes: bool = True,
fontsize: int = 12,
) -> None:
"""Visualize the loaded training data from the resource."""
from rasa.shared.core.training_data.visualization import visualize_stories
from rasa.shared.core.training_data import loading
# if the user doesn't provide a max history, we will use the
# largest value from any policy
max_history = max_history or self._max_history()
story_steps = loading.load_data_from_resource(resource_name,
self.domain)
await visualize_stories(
story_steps,
self.domain,
output_file,
max_history,
self.interpreter,
nlu_training_data,
should_merge_nodes,
fontsize,
)
def visualize(
domain_path: Text,
stories_path: Text,
nlu_data_path: Text,
output_path: Text,
max_history: int,
) -> None:
"""Visualizes stories as graph.
Args:
domain_path: Path to the domain file.
stories_path: Path to the stories files.
nlu_data_path: Path to the NLU training data which can be used to interpolate
intents with actual examples in the graph.
output_path: Path where the created graph should be persisted.
max_history: Max history to use for the story visualization.
"""
import rasa.shared.core.training_data.visualization
try:
domain = Domain.load(domain_path)
except InvalidDomain as e:
print_error(
f"Could not load domain due to: '{e}'. To specify a valid domain path use "
f"the '--domain' argument.")
return
# this is optional, only needed if the `/greet` type of
# messages in the stories should be replaced with actual
# messages (e.g. `hello`)
if nlu_data_path is not None:
import rasa.shared.nlu.training_data.loading
nlu_training_data = rasa.shared.nlu.training_data.loading.load_data(
nlu_data_path)
else:
nlu_training_data = None
logger.info("Starting to visualize stories...")
telemetry.track_visualization()
story_steps = loading.load_data_from_resource(stories_path, domain)
rasa.shared.core.training_data.visualization.visualize_stories(
story_steps,
domain,
output_path,
max_history,
nlu_training_data=nlu_training_data,
)
full_output_path = "file://{}".format(os.path.abspath(output_path))
logger.info(f"Finished graph creation. Saved into {full_output_path}")
import webbrowser
webbrowser.open(full_output_path)
def test_story_visualization_with_merging(domain: Domain):
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
generated_graph = visualization.visualize_stories(story_steps,
domain,
output_file=None,
max_history=3,
should_merge_nodes=True)
assert 15 < len(generated_graph.nodes()) < 33
assert 20 < len(generated_graph.edges()) < 33
def test_merge_nodes(domain: Domain, tmp_path: Path):
from os.path import isfile
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
out_file = str(tmp_path / "graph.html")
visualization.visualize_stories(
story_steps,
domain,
output_file=out_file,
max_history=3,
should_merge_nodes=True,
)
assert isfile(out_file)
async def test_story_visualization(domain: Domain, tmp_path: Path):
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
out_file = str(tmp_path / "graph.html")
generated_graph = await visualize_stories(
story_steps,
domain,
output_file=out_file,
max_history=3,
should_merge_nodes=False,
)
assert len(generated_graph.nodes()) == 51
assert len(generated_graph.edges()) == 56
def test_story_visualization(domain: Domain, tmp_path: Path):
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
out_file = tmp_path / "graph.html"
generated_graph = visualization.visualize_stories(
story_steps,
domain,
output_file=str(out_file),
max_history=3,
should_merge_nodes=False,
)
assert str(None) not in out_file.read_text()
assert "/affirm" in out_file.read_text()
assert len(generated_graph.nodes()) == 51
assert len(generated_graph.edges()) == 56
def extract_story_graph(
resource_name: Text,
domain: "Domain",
exclusion_percentage: Optional[int] = None) -> "StoryGraph":
"""Loads training stories / rules from file or directory.
Args:
resource_name: Path to file or directory.
domain: The model domain.
exclusion_percentage: Percentage of stories which should be dropped. `None`
if all training data should be used.
Returns:
The loaded training data as graph.
"""
from rasa.shared.core.training_data.structures import StoryGraph
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
resource_name, domain, exclusion_percentage=exclusion_percentage)
return StoryGraph(story_steps)
def test_graph_persistence(domain: Domain, tmp_path: Path):
from os.path import isfile
from networkx.drawing import nx_pydot
import rasa.shared.core.training_data.loading as core_loading
story_steps = core_loading.load_data_from_resource(
"data/test_yaml_stories/stories.yml", domain)
out_file = str(tmp_path / "graph.html")
generated_graph = visualization.visualize_stories(
story_steps,
domain,
output_file=out_file,
max_history=3,
should_merge_nodes=False,
)
generated_graph = nx_pydot.to_pydot(generated_graph)
assert isfile(out_file)
content = rasa.shared.utils.io.read_file(out_file)
assert "isClient = true" in content
assert "graph = `{}`".format(generated_graph.to_string()) in content
