refactor interpreter usage in story visualization

changelog/9766.removal.md

@@ -0,0 +1,2 @@
+`rasa.core.agent.Agent.visualize` was removed. Please use `rasa visualize` or
+`rasa.core.visualize.visualize` instead.

rasa/cli/arguments/visualize.py

@@ -1,7 +1,6 @@
 import argparse
 
 from rasa.cli.arguments.default_arguments import (
-    add_config_param,
     add_domain_param,
     add_stories_param,
     add_out_param,
@@ -10,9 +9,9 @@
 
 
 def set_visualize_stories_arguments(parser: argparse.ArgumentParser) -> None:
+    """Sets the CLI arguments for `rasa data visualize."""
     add_domain_param(parser)
     add_stories_param(parser)
-    add_config_param(parser)
 
     add_out_param(
         parser,

rasa/cli/visualize.py

@@ -37,8 +37,6 @@ def visualize_stories(args: argparse.Namespace) -> None:
     if args.nlu is None and os.path.exists(DEFAULT_DATA_PATH):
         args.nlu = rasa.shared.data.get_nlu_directory(DEFAULT_DATA_PATH)
 
-    rasa.utils.common.run_in_loop(
-        rasa.core.visualize.visualize(
-            args.config, args.domain, args.stories, args.nlu, args.out, args.max_history
-        )
+    rasa.core.visualize.visualize(
+        args.domain, args.stories, args.nlu, args.out, args.max_history
     )

rasa/core/agent.py

@@ -36,7 +36,6 @@
 from rasa.nlu.utils import is_url
 from rasa.shared.exceptions import RasaException
 import rasa.shared.utils.io
-from rasa.shared.nlu.training_data.training_data import TrainingData
 from rasa.utils.endpoints import EndpointConfig
 
 from rasa.core.tracker_store import TrackerStore
@@ -499,36 +498,6 @@ def _set_fingerprint(self, fingerprint: Optional[Text] = None) -> None:
         else:
             self.fingerprint = uuid.uuid4().hex
 
-    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."""
-        # TODO: This needs to be fixed to not use the interpreter
-        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,
-        )
-
     @staticmethod
     def _create_tracker_store(
         store: Optional[TrackerStore], domain: Domain

rasa/core/visualize.py

@@ -3,35 +3,34 @@
 from typing import Text
 
 from rasa import telemetry
+from rasa.shared.core.training_data import loading
 from rasa.shared.utils.cli import print_error
-
-from rasa.shared.core.domain import InvalidDomain
+from rasa.shared.core.domain import InvalidDomain, Domain
 
 logger = logging.getLogger(__name__)
 
 
-async def visualize(
-    config_path: Text,
+def visualize(
     domain_path: Text,
     stories_path: Text,
     nlu_data_path: Text,
     output_path: Text,
     max_history: int,
 ) -> None:
-    from rasa.core.agent import Agent
-    from rasa.core import config
+    """Visualizes stories as graph.
 
-    try:
-        policies = config.load(config_path)
-    except Exception as e:
-        print_error(
-            f"Could not load config due to: '{e}'. To specify a valid config file use "
-            f"the '--config' argument."
-        )
-        return
+    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:
-        agent = Agent(domain=domain_path, policies=policies)
+        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 "
@@ -53,8 +52,14 @@ async def visualize(
 
     logger.info("Starting to visualize stories...")
     telemetry.track_visualization()
-    await agent.visualize(
-        stories_path, output_path, max_history, nlu_training_data=nlu_training_data
+
+    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))

rasa/shared/core/training_data/visualization.py

@@ -4,6 +4,7 @@
 from typing import Any, Text, List, Dict, Optional, TYPE_CHECKING, Set
 
 import rasa.shared.utils.io
+from rasa.shared.constants import INTENT_MESSAGE_PREFIX
 from rasa.shared.core.constants import ACTION_LISTEN_NAME
 from rasa.shared.core.domain import Domain
 from rasa.shared.core.events import UserUttered, ActionExecuted, Event
@@ -14,7 +15,6 @@
     INTENT,
     TEXT,
     ENTITY_ATTRIBUTE_TYPE,
-    ENTITIES,
     INTENT_NAME_KEY,
 )
 
@@ -58,25 +58,15 @@ def _contains_same_entity(entities: Dict[Text, Any], e: Dict[Text, Any]) -> bool
         ) != e.get(ENTITY_ATTRIBUTE_VALUE)
 
     def message_for_data(self, 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."""
-
+        """Find a data sample with the same intent."""
         if structured_info.get(INTENT) is not None:
             intent_name = structured_info.get(INTENT, {}).get(INTENT_NAME_KEY)
             usable_examples = self.mapping.get(intent_name, [])[:]
             random.shuffle(usable_examples)
-            for example in usable_examples:
-                entities = {
-                    e.get(ENTITY_ATTRIBUTE_TYPE): e.get(ENTITY_ATTRIBUTE_VALUE)
-                    for e in example.get(ENTITIES, [])
-                }
-                for e in structured_info.get(ENTITIES, []):
-                    if self._contains_same_entity(entities, e):
-                        break
-                else:
-                    return example.get(TEXT)
+
+            if usable_examples:
+                return usable_examples[0].get(TEXT)
+
         return structured_info.get(TEXT)
 
 
@@ -265,13 +255,12 @@ def _merge_equivalent_nodes(graph: "networkx.MultiDiGraph", max_history: int) ->
                         graph.remove_node(j)
 
 
-async def _replace_edge_labels_with_nodes(
-    graph: "networkx.MultiDiGraph",
-    next_id: int,
-    interpreter,
-    nlu_training_data: "TrainingData",
+def _replace_edge_labels_with_nodes(
+    graph: "networkx.MultiDiGraph", next_id: int, nlu_training_data: "TrainingData",
 ) -> None:
-    """User messages are created as edge labels. This removes the labels and
+    """Replaces edge labels with nodes.
+
+    User messages are created as edge labels. This removes the labels and
     creates nodes instead.
 
     The algorithms (e.g. merging) are simpler if the user messages are labels
@@ -287,11 +276,14 @@ async def _replace_edge_labels_with_nodes(
     edges = list(graph.edges(keys=True, data=True))
     for s, e, k, d in edges:
         if k != EDGE_NONE_LABEL:
-            if message_generator and d.get("label", k) is not None:
-                parsed_info = await interpreter.parse(d.get("label", k))
+            label = d.get("label", k)
+
+            if message_generator:
+                parsed_info = {TEXT: label}
+                if label.startswith(INTENT_MESSAGE_PREFIX):
+                    parsed_info[INTENT] = {INTENT_NAME_KEY: label[1:]}
+
                 label = message_generator.message_for_data(parsed_info)
-            else:
-                label = d.get("label", k)
             next_id += 1
             graph.remove_edge(s, e, k)
             graph.add_node(
@@ -411,12 +403,11 @@ def _add_message_edge(
     )
 
 
-async def visualize_neighborhood(
+def visualize_neighborhood(
     current: Optional[List[Event]],
     event_sequences: List[List[Event]],
     output_file: Optional[Text] = None,
     max_history: int = 2,
-    interpreter=None,
     nlu_training_data: Optional["TrainingData"] = None,
     should_merge_nodes: bool = True,
     max_distance: int = 1,
@@ -446,10 +437,8 @@ async def visualize_neighborhood(
                 idx -= 1
                 break
             if isinstance(el, UserUttered):
-                if not el.intent:
-                    message = await interpreter.parse(el.text)
-                else:
-                    message = el.parse_data
+                message = el.parse_data
+                message[TEXT] = f"{INTENT_MESSAGE_PREFIX}{el.intent_name}"
             elif (
                 isinstance(el, ActionExecuted) and el.action_name != ACTION_LISTEN_NAME
             ):
@@ -507,9 +496,7 @@ async def visualize_neighborhood(
 
     if should_merge_nodes:
         _merge_equivalent_nodes(graph, max_history)
-    await _replace_edge_labels_with_nodes(
-        graph, next_node_idx, interpreter, nlu_training_data
-    )
+    _replace_edge_labels_with_nodes(graph, next_node_idx, nlu_training_data)
 
     _remove_auxiliary_nodes(graph, special_node_idx)
 
@@ -538,12 +525,11 @@ def _remove_auxiliary_nodes(
                 ps.add(pred)
 
 
-async def visualize_stories(
+def visualize_stories(
     story_steps: List[StoryStep],
     domain: Domain,
     output_file: Optional[Text],
     max_history: int,
-    interpreter=None,  # TODO: Fix this to use processor:
     nlu_training_data: Optional["TrainingData"] = None,
     should_merge_nodes: bool = True,
     fontsize: int = 12,
@@ -587,12 +573,11 @@ async def visualize_stories(
     completed_trackers = g.generate()
     event_sequences = [t.events for t in completed_trackers]
 
-    graph = await visualize_neighborhood(
+    graph = visualize_neighborhood(
         None,
         event_sequences,
         output_file,
         max_history,
-        interpreter,
         nlu_training_data,
         should_merge_nodes,
         max_distance=1,

tests/cli/test_rasa_visualize.py

@@ -6,8 +6,7 @@ def test_visualize_help(run: Callable[..., RunResult]):
     output = run("visualize", "--help")
 
     help_text = """usage: rasa visualize [-h] [-v] [-vv] [--quiet] [-d DOMAIN] [-s STORIES]
-                      [-c CONFIG] [--out OUT] [--max-history MAX_HISTORY]
-                      [-u NLU]"""
+                      [--out OUT] [--max-history MAX_HISTORY] [-u NLU]"""
 
     lines = help_text.split("\n")
     # expected help text lines should appear somewhere in the output

tests/core/test_training.py

@@ -10,7 +10,6 @@
 from rasa.shared.core.domain import Domain
 from rasa.core.policies.ted_policy import TEDPolicy
 
-from rasa.shared.core.training_data.visualization import visualize_stories
 import rasa.model_training
 import rasa.shared.utils.io
 
@@ -22,40 +21,6 @@ def test_load_training_data_reader_not_found_throws(tmp_path: Path, domain: Doma
         training.load_data(str(tmp_path), domain)
 
 
-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
-
-
-async 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 = await 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_training_script_with_restart_stories(tmp_path: Path, domain_path: Text):
     model_file = rasa.model_training.train_core(
         domain_path,