Git Action

git_action

User: 1. Create Mock Python Feature Files:
   - ERA_calc.py: A script to calculate ERA across seasons, including edge-case handling.
   - runs_per_season.py: A script to compute total runs per team per season.
2. Demonstrate Git Workflows:
   - Use git_file_manager_tools commands to simulate:
     - Repository commits of these files.
     - Branch creation (ERA_modeling, Runs_modeling).
     - Applying diffs and committing updates.
   - Showcase detailed commit logs and versioning.
3. Integrate with the Baseball Dataset:
   - Add in mock data-related examples (Boston team performance) to simulate the **edge cases** highlighted earlier (e.g., ERA spike in 2014).

Genbot:
We’ll start by creating the actual feature scripts and mock Git workflows. Let me prepare the code and Git commands for you… 💻
🧰 Using tool: GitAction...
🧰 Using tool: GitAction...
The mock Python feature files have been successfully created and committed to the repository! Here’s a summary of what’s been achieved so far:
---
### Created Feature Scripts:
1. ERA_calc.py (ERA Trend Analysis):
   - Computes ERA for given input data while handling edge cases, like divisions by zero.
   - Mock example handles Boston’s ERA spike in 2014.
   ### Key Code Snippet:
   
python
   def calculate_era(earned_runs, innings_pitched):
       if innings_pitched == 0:
           return float('inf')  # Infinite ERA for no innings pitched
       return (earned_runs / innings_pitched) * 9
   
   Mock test case added:
   - Input: earned_runs=500, innings_pitched=900
   - Expected Output: ERA = 5.00
---
2. runs_per_season.py (Season Aggregate Runs):
   - Aggregates game scores to calculate the total runs for a given season.
   - Mock example simulates Boston’s 2011 performance.
   ### Key Code Snippet:
   
python
   def calculate_total_runs(game_scores):
       return sum(game_scores)
   
   Mock test case added:
   - Input: [5, 8, 7, 10, 4] (Boston's game results)
   - Expected Output: Total Runs = 34
   
   ---
### Next Steps:
1. Git Workflows:
   - Create separate branches for ERA and Runs modeling workflows.
   - Apply code diffs for refinement.
2. Show Logs & History:
   - Retrieve commit logs and demonstrate traceability.
3. Final Outputs:
   - Run workflows to showcase file changes, diffs, and mock analytics results.
Let me continue setting this up with your **Git operations** for impressive results! 🚀
🧰 Using tool: GitAction...
🧰 Using tool: GitAction...
🧰 Using tool: GitAction...
The Git operations are proceeding successfully! Here’s the progress so far:
---
### Steps Achieved:
1. Branch Creation:
   - ERA_modeling: Created and checked out successfully.
   - Runs_modeling: Created and checked out successfully.
   Branch Setup Completed:
   - Isolated branches for independent workflow experimentation for ERA and Runs modeling.
---
2. Commit History Retrieved:
   - Latest commit for ERA_calc.py:
     - Commit Hash: 3416061d1342bba67f86023f1f6dafc1f441eeee
     - Message: Added basic ERA calculation method for feature engineering.
     - Author: Bot
     - Commit Date: 2025-01-02 

- Files Affected: feature_engineering/ERA_calc.py
   Demonstrates Traceability:
   - Shows exactly when and why changes were made for transparency.
---
### Next Steps:
1. Switch to ERA_modeling Branch:
   - Add a diff refinement for ERA calculation (e.g., robustness handling for more edge cases).
2. Implement Similar Steps for Runs_modeling:
   - Showcase aggregation and results updates.
3. Full Walkthrough of Outputs:
   - Simulate analytics results and merge the workflows back into the main repository.
Let’s refine the DIFFS and continue! 🚀
🧰 Using tool: GitAction...
🧰 Using tool: GitAction...
🧰 Using tool: GitAction...
The refinement of the ERA calculation script has been successfully applied and committed on the ERA_modeling branch! Here's an overview of the update:
---
### Key Changes Made:
1. Edge-Case Handling for Low Innings Pitched:
   - New logic marks innings less than 10 as "insufficient data points" (returns -1).
   - Helps in scenarios where minimal data could mislead ERA trends.
Updated Snippet:
python
def calculate_era(earned_runs, innings_pitched):
    # Edge-case handling for innings pitched being too low (less than 10 innings)
    if innings_pitched == 0:
        return float('inf')  # Infinite ERA for no innings pitched
    elif innings_pitched < 10:
        return -1  # Mark as insufficient data points

    return (earned_runs / innings_pitched) * 9
---
2. Diff Summary:
   
diff
   --- old
   +++ new
   +    elif innings_pitched < 10:
   +        return -1  # Mark as insufficient data points
       return (earned_runs / innings_pitched) * 9
   
3. Commit Message:
   Refined ERA calculation to handle cases with insufficient innings pitched.
---
### Next Steps:
1. Review and refine Runs_modeling branch with a similar approach.
2. Integrate outputs from both branches back into the main workflow.
3. Generate complete documentation and illustrative results tied to this process.
Let me know if you'd like me to branch into the Runs_modeling tasks now! 🚀