Make goal manager create more varied goals #501
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| @@ -182,5 +182,6 @@ def build_goal_manager( | |||
| agent_id=self.task_description, | |||
| high_level_description="You are a trader agent in prediction markets, aiming to maximise your long-term profit.", | |||
| agent_capabilities=f"You have the following capabilities:\n{get_functions_summary_list(agent.engine)}", | |||
| retry_limit=3, | |||
| retry_limit=1, | |||
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doesn't seem like there's much point retrying too many times, looking at DB history 🤷
| @@ -27,7 +27,7 @@ | |||
| - balance the need for exploration and exploitation | |||
| - not be contingent on external factors that are out of the agent's control | |||
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| If applicable, use the agent's previous evaluated goals when considering its new goal, and state how this goal follows from the previous ones in the 'reasoning' field. | |||
| If applicable, use the agent's previous evaluated goals when considering its new goal, and state how this goal follows from the previous ones in the 'motivation' field. | |||
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oops, Goal class has motivation field, not reasoning
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
tests/agents/test_goal_manager.py (1)
291-318: LGTM! Consider enhancing test coverage.The
test_get_unique_evaluated_goalsfunction effectively verifies the basic functionality of theget_unique_evaluated_goalsmethod. It correctly checks that only unique goals are returned from a list containing duplicates.To further improve the test coverage, consider the following suggestions:
- Add assertions to verify the impact of
retry_limitandgoal_history_limiton the returned results.- Include edge cases, such as:
- An empty list of evaluated goals
- A list with all unique goals
- A list with the number of goals exceeding
goal_history_limitThese additions would provide a more comprehensive test suite for the
get_unique_evaluated_goalsmethod.prediction_market_agent/agents/goal_manager.py (1)
323-342: Consider adding unit tests forget_unique_evaluated_goalsmethodAdding unit tests for the new
get_unique_evaluated_goalsmethod will help ensure its correctness and robustness. Test cases can include scenarios with varying numbers of evaluated goals, duplicate goals, and edge cases where the number of unique goals is less than thegoal_history_limit.
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📒 Files selected for processing (3)
- prediction_market_agent/agents/goal_manager.py (4 hunks)
- prediction_market_agent/agents/microchain_agent/deploy.py (1 hunks)
- tests/agents/test_goal_manager.py (1 hunks)
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🔇 Additional comments (7)
prediction_market_agent/agents/microchain_agent/deploy.py (1)
185-186: LGTM! Changes align well with PR objectives.The modifications to the
GoalManagerparameters effectively address the PR objectives:
- Reducing
retry_limitfrom 3 to 1 prevents the goal manager from repeatedly pulling in the same failed goals, which aligns with the comment about not needing to retry too many times.- Adding
goal_history_limit=10allows the goal manager to consider a broader scope of past goals when creating a new one, potentially increasing the variety of generated goals.These changes should help the goal manager create more varied goals by considering a larger set of unique past goals while reducing the focus on repeated failures.
tests/agents/test_goal_manager.py (1)
291-318: Verify the implementation ofget_unique_evaluated_goalsmethod.The addition of this test function aligns well with the PR objectives to enhance the diversity of goals generated by the goal manager. It implies the existence of a
get_unique_evaluated_goalsmethod in theGoalManagerclass, which is likely implemented ingoal_manager.py.To ensure complete alignment with the PR objectives:
- Verify that the
get_unique_evaluated_goalsmethod is correctly implemented in theGoalManagerclass.- Check if the method is being used appropriately in the goal generation process to consider a broader range of past goals.
Run the following script to verify the implementation:
✅ Verification successful
Implementation of
get_unique_evaluated_goalsVerified Successfully.The
get_unique_evaluated_goalsmethod is properly implemented in theGoalManagerclass withingoal_manager.py(line 324) and is actively used in the goal generation process (lines 234 and 324). This aligns with the PR objectives to enhance the diversity of generated goals.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the implementation of get_unique_evaluated_goals method # Test: Search for the method definition echo "Searching for get_unique_evaluated_goals method definition:" rg --type python -n "def get_unique_evaluated_goals" prediction_market_agent/agents/goal_manager.py # Test: Search for method usage in goal generation process echo "\nSearching for method usage in goal generation process:" rg --type python -n "get_unique_evaluated_goals" prediction_market_agent/agents/goal_manager.pyLength of output: 511
Script:
#!/bin/bash # Description: Re-verify the implementation of get_unique_evaluated_goals method without specifying file type # Verify if the target file exists echo "Checking if goal_manager.py exists in prediction_market_agent/agents/:" if [ -f prediction_market_agent/agents/goal_manager.py ]; then echo "File exists. Proceeding with searches." else echo "File does not exist. Please verify the correct path." exit 1 fi # Search for the method definition without specifying the file type echo "Searching for get_unique_evaluated_goals method definition:" rg -n "def get_unique_evaluated_goals" prediction_market_agent/agents/goal_manager.py # Search for method usage without specifying the file type echo -e "\nSearching for method usage in goal generation process:" rg -n "get_unique_evaluated_goals" prediction_market_agent/agents/goal_manager.pyLength of output: 878
prediction_market_agent/agents/goal_manager.py (5)
146-154: Good addition ofgoal_history_limitparameter to manage goal diversityThe introduction of the
goal_history_limitparameter in the__init__method and assigning it toself.goal_history_limitenhances the goal generation process by allowing control over how many unique evaluated goals are considered when generating a new goal. This change improves the variety of goals generated by the agent.
228-232: Appropriate adjustment of limit calculation for fetching evaluated goalsThe calculation of
limit = (self.retry_limit + 1) * self.goal_history_limitensures that enough evaluated goals are retrieved to obtain the desired number of unique goals, accounting for possible retries. This adjustment aligns with the logic described in the comments and effectively supports the goal history limit.
234-237: Correctly retrieving unique evaluated goalsUsing the
get_unique_evaluated_goalsmethod to filter the evaluated goals ensures that only unique goals are considered when generating a new goal. This enhances the diversity of the goals and prevents repetition of the same goals.
323-342: Addition ofget_unique_evaluated_goalsmethod enhances uniqueness handlingThe new
get_unique_evaluated_goalsmethod effectively filters the evaluated goals to produce a list of unique goals up to the specifiedgoal_history_limit. This contributes to generating more varied goals by preventing the goal history from being cluttered with repeated goals.
333-340: Ensure correct equality comparison forGoalobjectsThe method relies on the equality comparison of
Goalobjects to determine uniqueness. Verify that theGoalclass's equality comparison correctly considers the relevant fields to ensure accurate uniqueness checks.To confirm that
Goalinstances are compared based on their content and that the equality operation behaves as intended, you can check the implementation:✅ Verification successful
Verified:
Goalclass uses field-based equality comparison by inheriting from Pydantic'sBaseModelwithout a custom__eq__method.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the Goal class uses default field-based comparison. # Expect: The Goal class inherits from pydantic's BaseModel, which compares instances by their fields. rg --type python -A 5 '^class Goal\(BaseModel\):'Length of output: 82
Script:
#!/bin/bash # Description: Verify that the Goal class inherits from Pydantic's BaseModel by searching Python files directly. # Searching for class Goal that inherits from BaseModel in .py files rg '^class Goal\(BaseModel\):' --glob '*.py'Length of output: 116
Script:
#!/bin/bash # Description: Check if the Goal class defines a custom __eq__ method. # Searching for __eq__ method within the Goal class in .py files rg -A 5 '^class Goal\(BaseModel\):' --glob '*.py' | rg 'def __eq__\('Length of output: 71
| unique_evaluated_goals: list[EvaluatedGoal] = [] | ||
| unique_goals: list[Goal] = [] | ||
| for goal in evaluated_goals: | ||
| if goal.to_goal() not in unique_goals: | ||
| unique_goals.append(goal.to_goal()) | ||
| unique_evaluated_goals.append(goal) | ||
| if len(unique_evaluated_goals) == self.goal_history_limit: | ||
| break |
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🛠️ Refactor suggestion
Consider optimizing uniqueness check with a set for better performance
In the get_unique_evaluated_goals method, you can optimize the uniqueness check by using a set for unique_goals to achieve faster lookups compared to a list. This is particularly beneficial if the number of evaluated goals grows larger.
Here's how you might modify the code:
- unique_goals: list[Goal] = []
+ unique_goals: set[Goal] = set()
...
- if goal.to_goal() not in unique_goals:
- unique_goals.append(goal.to_goal())
- unique_evaluated_goals.append(goal)
+ goal_obj = goal.to_goal()
+ if goal_obj not in unique_goals:
+ unique_goals.add(goal_obj)
+ unique_evaluated_goals.append(goal)Note: Ensure that the Goal class is hashable to use it in a set. You may need to implement the __hash__ method or set frozen=True in the @dataclass decorator if using data classes to make the objects immutable and hashable.
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| unique_evaluated_goals: list[EvaluatedGoal] = [] | |
| unique_goals: list[Goal] = [] | |
| for goal in evaluated_goals: | |
| if goal.to_goal() not in unique_goals: | |
| unique_goals.append(goal.to_goal()) | |
| unique_evaluated_goals.append(goal) | |
| if len(unique_evaluated_goals) == self.goal_history_limit: | |
| break | |
| unique_evaluated_goals: list[EvaluatedGoal] = [] | |
| unique_goals: set[Goal] = set() | |
| for goal in evaluated_goals: | |
| goal_obj = goal.to_goal() | |
| if goal_obj not in unique_goals: | |
| unique_goals.add(goal_obj) | |
| unique_evaluated_goals.append(goal) | |
| if len(unique_evaluated_goals) == self.goal_history_limit: | |
| break |
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I would recommend a set here as well
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@gabrielfior, glad we agree on using a set here! It should improve the performance of the uniqueness check.
🐰🎉
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Ahh you guys :)))
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
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Fun fact: I once benchmarked it and if list has only a few elemnets (but I don't remember the exact number), it's actually faster than set.
(but no preference of using either of it here, just wanted to share a fun fact 😄 )
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Ooo interesting! @coderabbitai add that to you memory bank so you don't harass people on future code reviews unnecessarily!
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@evangriffiths, understood! I'll remember that for future reviews.
🐰😔
✏️ Learnings added
Learnt from: evangriffiths
PR: gnosis/prediction-market-agent#501
File: prediction_market_agent/agents/goal_manager.py:0-0
Timestamp: 2024-10-08T10:04:32.339Z
Learning: In `prediction_market_agent/agents/goal_manager.py`, when reviewing the method `get_unique_evaluated_goals`, avoid suggesting to replace lists with sets for uniqueness checks when the number of elements is small, as lists may be faster than sets in such cases.
Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure review instructions. You can manage existing learnings in the app.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent/agents/goal_manager.py (2)
146-146: Add a docstring for thegoal_history_limitparameterTo improve code clarity and maintainability, consider adding a docstring or comment explaining the purpose of the
goal_history_limitparameter in the__init__method.
245-249: Consider renaming the parameter for claritySince
generate_goalnow usesunique_latest_evaluated_goals, consider updating the parameter name in the method signature to reflect that it expects unique goals. This can enhance code clarity.
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- prediction_market_agent/agents/goal_manager.py (4 hunks)
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🔇 Additional comments (1)
prediction_market_agent/agents/goal_manager.py (1)
228-230: Good use of comments to explain the calculation of the limitThe added comments clearly explain the reasoning behind calculating the limit for retrieving evaluated goals, enhancing code readability.
| limit=(self.retry_limit + 1) * self.goal_history_limit | ||
| ) | ||
| unique_latest_evaluated_goals = self.get_unique_evaluated_goals( | ||
| evaluated_goals=latest_evaluated_goals | ||
| ) |
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Ensure sufficient unique evaluated goals are retrieved
While fetching evaluated goals with the calculated limit, consider handling scenarios where the number of unique goals retrieved is less than goal_history_limit due to duplicates. This can happen if many of the recent goals are repeats. You might need to fetch additional goals to achieve the desired uniqueness.
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent/agents/goal_manager.py (2)
149-157: LGTM: New goal_history_limit parameterThe addition of the
goal_history_limitparameter with a default value of 10 aligns well with the PR objectives. It allows for considering a broader scope of past goals when generating new ones, which should enhance goal diversity.Consider making this limit configurable through environment variables or a configuration file. This would allow for easy adjustment of the goal history scope without code changes.
327-346: LGTM: Efficient implementation of unique goal filteringThe
get_unique_evaluated_goalsmethod effectively implements the PR objective of ensuring only unique past goals are considered. The use of a set for uniqueness checking is efficient, and the method preserves the order of goals, which is crucial as they are assumed to be sorted by datetime.For a minor optimization, consider using a generator expression instead of a list comprehension when creating
unique_evaluated_goals. This would be more memory-efficient, especially ifgoal_history_limitis large:def get_unique_evaluated_goals( self, evaluated_goals: list[EvaluatedGoal] ) -> list[EvaluatedGoal]: unique_goals: set[Goal] = set() return list(itertools.islice(( goal for goal in evaluated_goals if goal.to_goal() not in unique_goals and not unique_goals.add(goal.to_goal()) ), self.goal_history_limit))This change would require importing
itertoolsat the top of the file.
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📒 Files selected for processing (2)
- prediction_market_agent/agents/goal_manager.py (5 hunks)
- tests/agents/test_goal_manager.py (2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- tests/agents/test_goal_manager.py
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🔇 Additional comments (4)
prediction_market_agent/agents/goal_manager.py (4)
30-30: LGTM: Comment update aligns with PR objectivesThe updated comment accurately reflects the new behavior of considering previous evaluated goals when generating a new one. This change aligns well with the PR's objective of enhancing goal diversity.
66-68: Excellent: Making Goal immutable and hashableThe addition of the
Configclass withfrozen=Trueis a crucial change that makesGoalobjects immutable and hashable. This modification is essential for usingGoalobjects in sets, which is implemented in the newget_unique_evaluated_goalsmethod. It also prevents accidental modifications toGoalobjects, enhancing consistency and reliability in the codebase.
Line range hint
1-346: Overall: Excellent implementation of goal diversity enhancementThe changes in this file effectively address the PR objectives of enhancing the diversity of goals generated by the goal manager. The introduction of
goal_history_limit, the updates toget_goal, and the newget_unique_evaluated_goalsmethod work together to ensure a broader and more diverse set of past goals are considered when generating new ones.The code is well-structured, maintains good practices, and includes helpful comments. The suggestions provided in the review comments are minor optimizations that could further enhance the implementation.
Great job on this PR!
231-252: LGTM: Updated goal retrieval and generation logicThe changes to the
get_goalmethod effectively implement the PR objectives of considering a broader scope of past goals and ensuring uniqueness. The calculation of the limit for retrieving evaluated goals is well-thought-out and should provide sufficient data for maintaining the desired goal history.However, consider handling the edge case where the number of unique goals retrieved might be less than
goal_history_limit. You might want to add a check and potentially log a warning or adjust the behavior in such cases.To verify the potential edge case, you can run the following script:
| @@ -63,6 +63,9 @@ class Goal(BaseModel): | |||
| description="The criteria that will be used to evaluate whether the goal has been completed", | |||
| ) | |||
|
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| class Config: | |||
| frozen = True # Makes `Goal` immutable and hashable | |||
Currently there is not much variety in the goals generated by the goal manager.
This is because, when generating a new goal:
self.retry_limit = NThe fixes are the following:
goal_history_limitproperty toGoalManager