Fix changes

HumanSignal · Nov 10, 2023 · 89e3381 · 89e3381
1 parent f5ba24c
commit 89e3381
Showing 1 changed file with 19 additions and 28 deletions.
diff --git a/adala/skills/skillset.py b/adala/skills/skillset.py
@@ -29,7 +29,7 @@ def apply(
         self,
         dataset: Union[Dataset, InternalDataFrame],
         runtime: Runtime,
-        improved_skill: Optional[str] = None,
+        improved_skill: Optional[str] = None
     ) -> InternalDataFrame:
         """
         Apply the skill set to a dataset using a specified runtime.
@@ -43,9 +43,7 @@ def apply(
         """
 
     @abstractmethod
-    def select_skill_to_improve(
-        self, accuracy: Mapping, accuracy_threshold: Optional[float] = 1.0
-    ) -> Optional[BaseSkill]:
+    def select_skill_to_improve(self, accuracy: Mapping, accuracy_threshold: Optional[float] = 1.0) -> Optional[BaseSkill]:
         """
         Select skill to improve based on accuracy.
 
@@ -97,7 +95,7 @@ class LinearSkillSet(SkillSet):
 
     Attributes:
         skills (Union[List[str], Dict[str, str], List[BaseSkill], Dict[str, BaseSkill]]): Provided skills
-        skill_sequence (List[str], optional): Ordered list of skill names indicating the order
+        skill_sequence (List[str], optional): Ordered list of skill names indicating the order 
                                               in which they should be acquired.
                                               By default, lexographical order of skill names is used.
         input_data_field (Optional[str], optional): Name of the input data field. Defaults to None.
@@ -119,11 +117,8 @@ class LinearSkillSet(SkillSet):
     skill_sequence: List[str] = None
     input_data_field: Optional[str] = None
 
-    @field_validator("skills", mode="before")
-    @classmethod
-    def skills_validator(
-        cls, v: Union[List[str], List[BaseSkill], Dict[str, BaseSkill]]
-    ) -> Dict[str, BaseSkill]:
+    @field_validator('skills', mode='before')
+    def skills_validator(cls, v: Union[List[str], List[BaseSkill], Dict[str, BaseSkill]]) -> Dict[str, BaseSkill]:
         """
         Validates and converts the skills attribute to a dictionary of skill names to BaseSkill instances.
 
@@ -145,7 +140,7 @@ def skills_validator(
                 skills[skill_name] = LLMSkill(
                     name=skill_name,
                     instructions=instructions,
-                    input_data_field=input_data_field,
+                    input_data_field=input_data_field
                 )
                 # Linear skillset creates skills pipeline - update input_data_field for next skill
                 input_data_field = skill_name
@@ -155,7 +150,7 @@ def skills_validator(
                 skills[skill_name] = LLMSkill(
                     name=skill_name,
                     instructions=instructions,
-                    input_data_field=input_data_field,
+                    input_data_field=input_data_field
                 )
                 # Linear skillset creates skills pipeline - update input_data_field for next skill
                 input_data_field = skill_name
@@ -169,8 +164,8 @@ def skills_validator(
             raise ValueError(f"skills must be a list or dictionary, not {type(skills)}")
         return skills
 
-    @model_validator(mode="after")
-    def skill_sequence_validator(self) -> "LinearSkillSet":
+    @model_validator(mode='after')
+    def skill_sequence_validator(self) -> 'LinearSkillSet':
         """
         Validates and sets the default order for the skill sequence if not provided.
         
@@ -181,11 +176,9 @@ def skill_sequence_validator(self) -> "LinearSkillSet":
             # use default skill sequence defined by lexicographical order
             self.skill_sequence = list(self.skills.keys())
         if len(self.skill_sequence) != len(self.skills):
-            raise ValueError(
-                f"skill_sequence must contain all skill names - "
-                f"length of skill_sequence is {len(self.skill_sequence)} "
-                f"while length of skills is {len(self.skills)}"
-            )
+            raise ValueError(f"skill_sequence must contain all skill names - "
+                             f"length of skill_sequence is {len(self.skill_sequence)} "
+                             f"while length of skills is {len(self.skills)}")
         return self
 
     def apply(
@@ -208,9 +201,7 @@ def apply(
         predictions = None
         if improved_skill:
             # start from the specified skill, assuming previous skills have already been applied
-            skill_sequence = self.skill_sequence[
-                self.skill_sequence.index(improved_skill) :
-            ]
+            skill_sequence = self.skill_sequence[self.skill_sequence.index(improved_skill):]
         else:
             skill_sequence = self.skill_sequence
         for i, skill_name in enumerate(skill_sequence):
@@ -223,10 +214,12 @@ def apply(
         return predictions
 
     def select_skill_to_improve(
-        self, accuracy: Mapping, accuracy_threshold: Optional[float] = 0.9
+        self,
+        accuracy: Mapping,
+        accuracy_threshold: Optional[float] = 1.0
     ) -> Optional[BaseSkill]:
         """
-        Selects the first skill in the sequence with accuracy below the threshold to improve.
+        Selects the skill with the lowest accuracy to improve.
 
         Args:
             accuracy (Mapping): Accuracy of each skill.
@@ -243,10 +236,8 @@ def __rich__(self):
         # TODO: move it to a base class and use repr derived from Skills
         text = f"[bold blue]Total Agent Skills: {len(self.skills)}[/bold blue]\n\n"
         for skill in self.skills.values():
-            text += (
-                f"[bold underline green]{skill.name}[/bold underline green]\n"
-                f"[green]{skill.instructions}[green]\n"
-            )
+            text += f'[bold underline green]{skill.name}[/bold underline green]\n' \
+                    f'[green]{skill.instructions}[green]\n'
         return text