Fix/general

README.md

@@ -23,12 +23,14 @@ Once you have installed all dependencies you are ready to go with:
 from nl2ltl import translate
 from nl2ltl.engines.rasa.core import RasaEngine
 from nl2ltl.filters.simple_filters import BasicFilter
+from nl2ltl.engines.utils import pretty
 
 engine = RasaEngine()
 filter = BasicFilter()
 utterance = "Eventually send me a Slack after receiving a Gmail"
 
 ltlf_formulas = translate(utterance, engine, filter)
+pretty(ltlf_formulas)
 ```
 
 The `translate` function takes a natural language utterance, an engine and an

nl2ltl/engines/rasa/helpers.py

@@ -4,9 +4,6 @@
 import glob
 import os
 from pathlib import Path
-from typing import Dict
-
-from pylogics.syntax.base import Formula
 
 
 def _get_latest_model(path: Path):
@@ -17,12 +14,3 @@ def _get_latest_model(path: Path):
     if len(list_of_files) == 0:
         return None
     return max(list_of_files, key=os.path.getctime)
-
-
-def pretty(result: Dict[Formula, float]):
-    """Pretty print Rasa output."""
-    print("=" * 150)
-    for k, v in result.items():
-        print(f"Declare Template: {str(k)}", end="\n")
-        print(f"English meaning:  {k.to_english()}", end="\n")
-        print(f"Confidence:       {str(v)}", end="\n\n")

nl2ltl/engines/utils.py

@@ -26,3 +26,12 @@ def _get_formulas(name: str, args: Dict[str, float]) -> Set[Formula]:
     grounding_func: Callable = grounding_map[str(class_name_match[0])]
     grounded_formulas: Set[Formula] = grounding_func(args)
     return grounded_formulas
+
+
+def pretty(result: Dict[Formula, float]):
+    """Pretty print Rasa output."""
+    print("=" * 150)
+    for k, v in result.items():
+        print(f"Declare Template: {str(k)}", end="\n")
+        print(f"English meaning:  {k.to_english()}", end="\n")
+        print(f"Confidence:       {str(v)}", end="\n\n")

tests/test_gpt3.py

@@ -7,7 +7,7 @@
 
 from nl2ltl import translate
 from nl2ltl.engines.gpt3.core import GPT3Engine
-from nl2ltl.filters.simple_filters import BasicFilter
+from nl2ltl.filters.simple_filters import BasicFilter, GreedyFilter
 
 from .conftest import UtterancesFixtures
 
@@ -22,9 +22,16 @@ def setup_class(cls):
         """
         cls.gpt3_engine = GPT3Engine()
         cls.basic_filter = BasicFilter()
+        cls.greedy_filter = GreedyFilter()
 
     @pytest.mark.parametrize("utterance", UtterancesFixtures.utterances)
-    def test_rasa_engine(self, utterance):
-        """Test Rasa engine for utterances."""
+    def test_gpt3_engine_basic(self, utterance):
+        """Test GPT-3 engine for utterances with basic filter."""
         output = translate(utterance, self.gpt3_engine, self.basic_filter)
         assert isinstance(output, Dict)
+
+    @pytest.mark.parametrize("utterance", UtterancesFixtures.utterances)
+    def test_gpt3_engine_greedy(self, utterance):
+        """Test GPT-3 engine for utterances with greedy filter."""
+        output = translate(utterance, self.gpt3_engine, self.greedy_filter)
+        assert isinstance(output, Dict)

tests/test_rasa.py

@@ -7,7 +7,7 @@
 
 from nl2ltl import translate
 from nl2ltl.engines.rasa.core import RasaEngine
-from nl2ltl.filters.simple_filters import BasicFilter
+from nl2ltl.filters.simple_filters import BasicFilter, GreedyFilter
 
 from .conftest import UtterancesFixtures
 
@@ -22,9 +22,16 @@ def setup_class(cls):
         """
         cls.rasa_engine = RasaEngine()
         cls.basic_filter = BasicFilter()
+        cls.greedy_filter = GreedyFilter()
 
     @pytest.mark.parametrize("utterance", UtterancesFixtures.utterances)
-    def test_rasa_engine(self, utterance):
-        """Test Rasa engine for utterances."""
+    def test_rasa_engine_basic(self, utterance):
+        """Test Rasa engine for utterances with basic filter."""
         output = translate(utterance, self.rasa_engine, self.basic_filter)
         assert isinstance(output, Dict)
+
+    @pytest.mark.parametrize("utterance", UtterancesFixtures.utterances)
+    def test_rasa_engine_greedy(self, utterance):
+        """Test Rasa engine for utterances with greedy filter."""
+        output = translate(utterance, self.rasa_engine, self.greedy_filter)
+        assert isinstance(output, Dict)