[FIX] Reset valid_optimization for each experiment

[mohammedahmed18]

User description

in experiment mode EXP0 valid_optimizations are not cleared before starting the EXP1 optimization

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PR Type

Bug fix

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Description

• Reset valid_optimizations each experiment iteration

• Prevent stale optimizations carryover across experiments

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File Walkthrough

	Relevant files
Bug fix	function_optimizer.py Reset valid_optimizations per experiment iteration              codeflash/optimization/function_optimizer.py Added reset of valid_optimizations inside loop Clears stale optimizations before determining best candidate +1/-0

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[github-actions]

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⏱️ Estimated effort to review: 1 🔵⚪⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Reset placement The reset of valid_optimizations happens after a continue, so if candidates is None the stale list isn’t cleared. Consider moving the reset to the top of the loop body to ensure it runs for every experiment. for _u, (candidates, exp_type) in enumerate( zip([optimizations_set.control, optimizations_set.experiment], ["EXP0", "EXP1"]) ): if candidates is None: continue self.valid_optimizations = [] # reset for each experiment

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
General	Clear list in-place Use an in-place clear operation to reset valid_optimizations to preserve any existing references and avoid unexpected side-effects from reassigning the list. codeflash/optimization/function_optimizer.py [1031] -self.valid_optimizations = [] # reset for each experiment +self.valid_optimizations.clear() # reset for each experiment Suggestion importance[1-10]: 5 __ Why: Using .clear() preserves the list identity and any external references to valid_optimizations, reducing unintended side-effects, although the change is a minor enhancement.	Low